Patients may need extended VTE prophylaxis, doc says

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Patients may need extended VTE prophylaxis, doc says

Thrombus

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SEATTLE—Patients who undergo surgery for lung cancer may have a higher risk of developing venous thromboembolism (VTE) than we thought, according to a

new study.

About 12% of the patients studied developed deep vein thrombosis (DVT), pulmonary embolism (PE), or both, although they had received VTE prophylaxis until hospital discharge.

Only about 21% of these patients showed symptoms of VTE, and the clots conferred a higher risk of mortality at 30 days.

“This study shows that a significant proportion of lung cancer surgery patients are at risk of VTE and indicates a need for future research into minimizing the occurrence of DVT and PE,” said investigator Yaron Shargall, MD, of McMaster University in Hamilton, Ontario, Canada.

“It is possible that extended use of blood thinners beyond hospital discharge may reduce the number of patients who experience these life-threatening events and may help to reduce the rates of death after lung surgery.”

Dr Shargall presented this viewpoint at the 95th Annual Meeting of the American Association for Thoracic Surgery.

For their study, he and his colleagues evaluated 157 patients who underwent thoracic surgery for primary lung cancer (89.9%) or metastatic cancer (6.3%).

All patients received unfractionated heparin or low-molecular-weight heparin and graduated compression stockings as VTE prophylaxis from the time of surgery until leaving the hospital.

Two weeks later, these patients were evaluated for signs and symptoms of VTE. The investigators evaluated clinical outcomes at 30 ± 5 days post-operatively using CT pulmonary angiography and bilateral Doppler venous ultrasonography.

Patients who had developed symptoms suggestive of VTE within the 30 days after surgery underwent urgent CT-PE examination and had a repeat scan 30 days after surgery if the first scan was negative. Patients with VTE were monitored and treated.

In all, there were 19 VTEs, a 12.1% incidence rate. These included 14 PEs (8.9%), 3 DVTs (1.9%), and 1 combined PE/DVT. One patient developed a massive left atrial thrombus originating from a surgical stump and died.

For all 157 patients, the 30-day mortality rate was 0.64%. For those with VTE, it was 5.2%.

“This demonstrates the clinical importance and relative fatality of VTE following lung cancer surgery,” Dr Shargall said.

All of the patients who were diagnosed with a VTE had undergone anatomic resections (lobectomy or segmentectomy), and most had primary lung cancer. The clots tended to form on the same side as the lung surgery. The majority of patients developed lung clots without forming DVTs beforehand.

The investigators examined factors that might distinguish patients who developed VTEs from those who did not and could not find differences in patient age, lung function, hospital length of stay, comorbidities, lung cancer stage, smoking status, or Caprini Score.

Among patients diagnosed with a VTE, only 4 (21.1%) showed symptoms. All the events were diagnosed after the patient left the hospital and only because these patients were screened for VTEs as part of the study.

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Thrombus

Photo by Andre E.X. Brown

SEATTLE—Patients who undergo surgery for lung cancer may have a higher risk of developing venous thromboembolism (VTE) than we thought, according to a

new study.

About 12% of the patients studied developed deep vein thrombosis (DVT), pulmonary embolism (PE), or both, although they had received VTE prophylaxis until hospital discharge.

Only about 21% of these patients showed symptoms of VTE, and the clots conferred a higher risk of mortality at 30 days.

“This study shows that a significant proportion of lung cancer surgery patients are at risk of VTE and indicates a need for future research into minimizing the occurrence of DVT and PE,” said investigator Yaron Shargall, MD, of McMaster University in Hamilton, Ontario, Canada.

“It is possible that extended use of blood thinners beyond hospital discharge may reduce the number of patients who experience these life-threatening events and may help to reduce the rates of death after lung surgery.”

Dr Shargall presented this viewpoint at the 95th Annual Meeting of the American Association for Thoracic Surgery.

For their study, he and his colleagues evaluated 157 patients who underwent thoracic surgery for primary lung cancer (89.9%) or metastatic cancer (6.3%).

All patients received unfractionated heparin or low-molecular-weight heparin and graduated compression stockings as VTE prophylaxis from the time of surgery until leaving the hospital.

Two weeks later, these patients were evaluated for signs and symptoms of VTE. The investigators evaluated clinical outcomes at 30 ± 5 days post-operatively using CT pulmonary angiography and bilateral Doppler venous ultrasonography.

Patients who had developed symptoms suggestive of VTE within the 30 days after surgery underwent urgent CT-PE examination and had a repeat scan 30 days after surgery if the first scan was negative. Patients with VTE were monitored and treated.

In all, there were 19 VTEs, a 12.1% incidence rate. These included 14 PEs (8.9%), 3 DVTs (1.9%), and 1 combined PE/DVT. One patient developed a massive left atrial thrombus originating from a surgical stump and died.

For all 157 patients, the 30-day mortality rate was 0.64%. For those with VTE, it was 5.2%.

“This demonstrates the clinical importance and relative fatality of VTE following lung cancer surgery,” Dr Shargall said.

All of the patients who were diagnosed with a VTE had undergone anatomic resections (lobectomy or segmentectomy), and most had primary lung cancer. The clots tended to form on the same side as the lung surgery. The majority of patients developed lung clots without forming DVTs beforehand.

The investigators examined factors that might distinguish patients who developed VTEs from those who did not and could not find differences in patient age, lung function, hospital length of stay, comorbidities, lung cancer stage, smoking status, or Caprini Score.

Among patients diagnosed with a VTE, only 4 (21.1%) showed symptoms. All the events were diagnosed after the patient left the hospital and only because these patients were screened for VTEs as part of the study.

Thrombus

Photo by Andre E.X. Brown

SEATTLE—Patients who undergo surgery for lung cancer may have a higher risk of developing venous thromboembolism (VTE) than we thought, according to a

new study.

About 12% of the patients studied developed deep vein thrombosis (DVT), pulmonary embolism (PE), or both, although they had received VTE prophylaxis until hospital discharge.

Only about 21% of these patients showed symptoms of VTE, and the clots conferred a higher risk of mortality at 30 days.

“This study shows that a significant proportion of lung cancer surgery patients are at risk of VTE and indicates a need for future research into minimizing the occurrence of DVT and PE,” said investigator Yaron Shargall, MD, of McMaster University in Hamilton, Ontario, Canada.

“It is possible that extended use of blood thinners beyond hospital discharge may reduce the number of patients who experience these life-threatening events and may help to reduce the rates of death after lung surgery.”

Dr Shargall presented this viewpoint at the 95th Annual Meeting of the American Association for Thoracic Surgery.

For their study, he and his colleagues evaluated 157 patients who underwent thoracic surgery for primary lung cancer (89.9%) or metastatic cancer (6.3%).

All patients received unfractionated heparin or low-molecular-weight heparin and graduated compression stockings as VTE prophylaxis from the time of surgery until leaving the hospital.

Two weeks later, these patients were evaluated for signs and symptoms of VTE. The investigators evaluated clinical outcomes at 30 ± 5 days post-operatively using CT pulmonary angiography and bilateral Doppler venous ultrasonography.

Patients who had developed symptoms suggestive of VTE within the 30 days after surgery underwent urgent CT-PE examination and had a repeat scan 30 days after surgery if the first scan was negative. Patients with VTE were monitored and treated.

In all, there were 19 VTEs, a 12.1% incidence rate. These included 14 PEs (8.9%), 3 DVTs (1.9%), and 1 combined PE/DVT. One patient developed a massive left atrial thrombus originating from a surgical stump and died.

For all 157 patients, the 30-day mortality rate was 0.64%. For those with VTE, it was 5.2%.

“This demonstrates the clinical importance and relative fatality of VTE following lung cancer surgery,” Dr Shargall said.

All of the patients who were diagnosed with a VTE had undergone anatomic resections (lobectomy or segmentectomy), and most had primary lung cancer. The clots tended to form on the same side as the lung surgery. The majority of patients developed lung clots without forming DVTs beforehand.

The investigators examined factors that might distinguish patients who developed VTEs from those who did not and could not find differences in patient age, lung function, hospital length of stay, comorbidities, lung cancer stage, smoking status, or Caprini Score.

Among patients diagnosed with a VTE, only 4 (21.1%) showed symptoms. All the events were diagnosed after the patient left the hospital and only because these patients were screened for VTEs as part of the study.

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Simpler, more cost-effective way to grow stem cells

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Simpler, more cost-effective way to grow stem cells

Binata Joddar, PhD

Photo courtesy of University

of Texas at El Paso

Researchers say they have developed a protocol to prepare human induced pluripotent stem (hiPS) cells using chemically fixed feeder cells.

This method saves time and money by avoiding the need for colony formation of live feeder cells, which is required by current conventional methods.

The new protocol challenges the theory that live feeder cells are required to provide nutrients to growing stem cells.

“We’ve proved an important phenomenon,” said Binata Joddar, PhD, of the University of Texas at El Paso. “And it suggests that these feeder cells, which are difficult to grow, may not be important at all for stem cell growth.”

Dr Joddar and her colleagues described the phenomenon in Journal of Materials Chemistry B.

Using 2.5% glutaraldehyde (GA) or formaldehyde (FA) for 10 minutes, the researchers prepared a niche matrix from autologus human dermal fibroblast (HDF) feeder cells.

They then introduced hiPS cells to the niche matrix, which adhered to and were maintained as colonies on the fixed feeder cells.

The colony doubling times of the cells grown this way were similar to those of hiPS cells grown on mitomycin-C-treated HDF or SNL feeders. (SNL cells are derived from mouse fibroblast STO cells transformed with a neomycin resistance gene.)

But the colony doubling time for the hiPS cells was shorter with the fixed feeder than for cells cultured on laminin-5.

The researchers also discovered that the average number of colonies per passage was signficiantly higher for hiPS cells cultured on fixed feeder cells compared to those cultured without feeders.

They noted hiPS cells cultured on gelatin did not grow beyond the first passage.

The team concluded that the two types of chemically fixed HDF feeder cells (HDF-glutaraldehyde and HDF-formaldehyde) can be used as substitutes for mitomycin-C-treated HDF feeders to culture hiPS cells.

This new method would not extend the doubling time, would save preparation time, and would avoid labor-intensive protocols to prepare.

In addition, after chemical fixation, the feeder cells are non-viable and cannot release active growth factors or chemokines into the cell culture. Therefore, fixed feeder cells can be refrigerated for long-term storage prior to use.

“Because feeder cells don’t need to stay alive in the process, we can store them at room temperature and spend less time cultivating them,” Dr Joddar said.

“This makes me think that we [could] use a nanomanufacturing approach to grow stem cells. We could mimic feeder cells’ nanotopology with 3-D printing techniques and skip using feeder cells altogether in the future.”

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Binata Joddar, PhD

Photo courtesy of University

of Texas at El Paso

Researchers say they have developed a protocol to prepare human induced pluripotent stem (hiPS) cells using chemically fixed feeder cells.

This method saves time and money by avoiding the need for colony formation of live feeder cells, which is required by current conventional methods.

The new protocol challenges the theory that live feeder cells are required to provide nutrients to growing stem cells.

“We’ve proved an important phenomenon,” said Binata Joddar, PhD, of the University of Texas at El Paso. “And it suggests that these feeder cells, which are difficult to grow, may not be important at all for stem cell growth.”

Dr Joddar and her colleagues described the phenomenon in Journal of Materials Chemistry B.

Using 2.5% glutaraldehyde (GA) or formaldehyde (FA) for 10 minutes, the researchers prepared a niche matrix from autologus human dermal fibroblast (HDF) feeder cells.

They then introduced hiPS cells to the niche matrix, which adhered to and were maintained as colonies on the fixed feeder cells.

The colony doubling times of the cells grown this way were similar to those of hiPS cells grown on mitomycin-C-treated HDF or SNL feeders. (SNL cells are derived from mouse fibroblast STO cells transformed with a neomycin resistance gene.)

But the colony doubling time for the hiPS cells was shorter with the fixed feeder than for cells cultured on laminin-5.

The researchers also discovered that the average number of colonies per passage was signficiantly higher for hiPS cells cultured on fixed feeder cells compared to those cultured without feeders.

They noted hiPS cells cultured on gelatin did not grow beyond the first passage.

The team concluded that the two types of chemically fixed HDF feeder cells (HDF-glutaraldehyde and HDF-formaldehyde) can be used as substitutes for mitomycin-C-treated HDF feeders to culture hiPS cells.

This new method would not extend the doubling time, would save preparation time, and would avoid labor-intensive protocols to prepare.

In addition, after chemical fixation, the feeder cells are non-viable and cannot release active growth factors or chemokines into the cell culture. Therefore, fixed feeder cells can be refrigerated for long-term storage prior to use.

“Because feeder cells don’t need to stay alive in the process, we can store them at room temperature and spend less time cultivating them,” Dr Joddar said.

“This makes me think that we [could] use a nanomanufacturing approach to grow stem cells. We could mimic feeder cells’ nanotopology with 3-D printing techniques and skip using feeder cells altogether in the future.”

Binata Joddar, PhD

Photo courtesy of University

of Texas at El Paso

Researchers say they have developed a protocol to prepare human induced pluripotent stem (hiPS) cells using chemically fixed feeder cells.

This method saves time and money by avoiding the need for colony formation of live feeder cells, which is required by current conventional methods.

The new protocol challenges the theory that live feeder cells are required to provide nutrients to growing stem cells.

“We’ve proved an important phenomenon,” said Binata Joddar, PhD, of the University of Texas at El Paso. “And it suggests that these feeder cells, which are difficult to grow, may not be important at all for stem cell growth.”

Dr Joddar and her colleagues described the phenomenon in Journal of Materials Chemistry B.

Using 2.5% glutaraldehyde (GA) or formaldehyde (FA) for 10 minutes, the researchers prepared a niche matrix from autologus human dermal fibroblast (HDF) feeder cells.

They then introduced hiPS cells to the niche matrix, which adhered to and were maintained as colonies on the fixed feeder cells.

The colony doubling times of the cells grown this way were similar to those of hiPS cells grown on mitomycin-C-treated HDF or SNL feeders. (SNL cells are derived from mouse fibroblast STO cells transformed with a neomycin resistance gene.)

But the colony doubling time for the hiPS cells was shorter with the fixed feeder than for cells cultured on laminin-5.

The researchers also discovered that the average number of colonies per passage was signficiantly higher for hiPS cells cultured on fixed feeder cells compared to those cultured without feeders.

They noted hiPS cells cultured on gelatin did not grow beyond the first passage.

The team concluded that the two types of chemically fixed HDF feeder cells (HDF-glutaraldehyde and HDF-formaldehyde) can be used as substitutes for mitomycin-C-treated HDF feeders to culture hiPS cells.

This new method would not extend the doubling time, would save preparation time, and would avoid labor-intensive protocols to prepare.

In addition, after chemical fixation, the feeder cells are non-viable and cannot release active growth factors or chemokines into the cell culture. Therefore, fixed feeder cells can be refrigerated for long-term storage prior to use.

“Because feeder cells don’t need to stay alive in the process, we can store them at room temperature and spend less time cultivating them,” Dr Joddar said.

“This makes me think that we [could] use a nanomanufacturing approach to grow stem cells. We could mimic feeder cells’ nanotopology with 3-D printing techniques and skip using feeder cells altogether in the future.”

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Patient Satisfaction Variance Prediction

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Demographic factors and hospital size predict patient satisfaction variance—implications for hospital value‐based purchasing

The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1
Overall patient satisfaction by population density decile. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores are segregated by population density deciles (representing 33 million people each). Population density increases along the grey scale. The composite score and 9 out of 10 HCAHPS dimensions demonstrate lower patient satisfaction as population density increases (darker shade). Abbreviations: Doc, doctor; Def Rec, definitely recommend.
Figure 2
Averaged Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores by state correlated with state population (Pop) density. Bivariate correlation of composite HCAHPS scores predicted by state population density without District of Columbia, r = −0.479, P < 0.001 (2‐tailed). This observed correlation informed the hypothesis that population density could predict for lower patient satisfaction via HCAHPS scores.

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

Data Collection

Publically available data were obtained from Hospital Compare,[20] American Hospital Directory,[21] and the US Census Bureau[22] websites. Twenty relevant US Census data categories were selected by their relevance for this study out of the 50 publically reported US Census categories, and included the following: county population, county population density, percent of population change over 1 year, poverty level (percent), income level per capita, median household income, average household size, travel time to work, percentage of high school or college graduates, non‐English primary language spoken at home, percentage of residents born outside of the United States, population percent in same residence for over 1 year, gender, race (white alone, white alone (not Hispanic or Latino), black or African American alone), population over 65 years old, and population under 18 years old.

HCAHPS Development

The HCAHPS survey is 32 questions in length, comprised of 10 evaluative dimensions. All short‐term, acute care, nonspecialty hospitals are invited to participate in the HCAHPS survey.

Data Analysis

Statistical analyses used the Statistical Package for Social Sciences version 16.0 for Windows (SPSS Inc., Chicago, IL). Data were checked for statistical assumptions, including normality, linearity of relationships, and full range of scores. Categories in both the Hospital Compare (HCAHPS) and US Census datasets were analyzed to assess their distribution curves. The category of population densities (per county) was converted to a logarithmic scale to account for a skewed distribution and long tail in the area of low population density. Data were subsequently merged into an Excel (Microsoft, Redmond, WA) spreadsheet using the VLookup function such that relevant 2010 census county data were added to each hospital's Hospital Compare data. Linear regression modeling was performed. Bivariate analysis was conducted (ENTER method) to determine the significant US Census data predictors for each of the 10 Hospital Compare dimensions including the composite overall satisfaction score. Significant predictors were then analyzed in a multivariate model (BACKWORDS method) for each Hospital Compare dimension and the composite average positive score. Models were assessed by determinates of correlation (adjusted R2) to assess for goodness of fit. Statistically significant predictor variables for overall patient satisfaction scores were then ranked according to their partial regression coefficients (standardized ).

A patient satisfaction predictive model was sought based upon significant predictors of aggregate percent positive HCAHPS scores. Various predictor combinations were formed based on their partial coefficients (ie, standardized coefficients); combinations were assessed based on their R2 values and assessed for colinearity. Combinations of partial coefficients included the 2, 4, and 8 most predictive variables as well the 2 most positive and negative predictors. They were then incorporated into a multivariate analysis model (FORWARD method) and assessed based on their adjusted R2 values. A 4‐variable combination (the 2 most predictive positive partial coefficients plus the 2 most predictive negative partial coefficients) was selected as a predictive model, and a formula predictive of the composite overall satisfaction score was generated. This formula (predicted patient satisfaction formula [PPSF]) predicts hospital patient satisfaction HCAHPS scores based on the 4 predictive variables for particular county and hospital characteristics. PPSF=KMV+BHB(HB)+BNE(NE)+BE(E)+BW(W)where KMV=coefficient constant (70.9), B=unstandardized coefficient (see Table 1 for values), HB=number of hospital beds, NE=proportion of non‐English speakers, E=education (proportion with bachelor's degree), and W=proportion identified as white race only.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Average Positive Score by County and Hospital Demographics
 BSE tP
  • NOTE: A multivariate linear regression model of statistically significant dimensions of patient satisfaction as determined by Hospital Consumer Assessment of Healthcare Providers and Systems scores is provided. The dependent variable is the composite of average patient satisfaction scores by hospital (3192 hospitals). Predictors (independent variables) were collected from US Census data for counties or county equivalents. All of the listed predictors (first column) are statistically significant. They are placed in order of partial regression coefficient contribution to the model from most positive to most negative contribution. Adjusted R2 (last row) is used to signify the goodness of fit. Abbreviations: , standardized (partial coefficient); B, unstandardized coefficient; P, statistical significance; SE, standard error; t, t statistic.

Educational attainmentbachelor's degree0.1570.0180.278.612<0.001
White alone percent 20120.090.0120.2357.587<0.001
Resident population percent under 18 years0.4040.04440.2099.085<0.001
Black or African American alone percent 20120.0830.0140.1915.936<0.001
Median household income 200720110.000030.000.0622.0270.043
Population density (log) 20100.2770.0830.0873.33330.001
Average travel time to work0.1070.0240.0884.366<0.001
Educational attainmenthigh school0.0820.0260.0883.1470.002
Average household size2.580.7270.1073.55<0.001
Total females percent 20120.4230.0670.1076.296<0.001
Percent nonEnglish speaking at home 200720110.0520.0180.142.9290.003
No. of hospital beds0.0060.000.21312.901<0.001
Adjusted R20.222    

The PPSF was then modified by weighting with the partial coefficient () to remove the bias in patient satisfaction generated by demographic and structural factors over which individual hospitals have limited or no control. This formula generated a Weighted Individual (hospital) Predicted Patient Satisfaction Score (WIPPSS). Application of this formula narrowed the predicted distribution of patient satisfaction for all hospitals across the country. WIPPSS=KMV+BHB(HB)(1HB)+BNE(NE)(1NE)+BE(E)(1E)+BW(W)(1W)where =standardized coefficient (see Table 1 for values).

To create an adjusted score with direct relevance to the reported patient satisfaction scores, the reported scores were multiplied by an adjustment factor that defines the difference between individual hospital‐weighted scores and the national mean HCAHPS score across the United States. This formula, the Weighted Individual (hospital) Patient Satisfaction Adjustment Score (WIPSAS), represents a patient satisfaction score adjusted for demographic and structural factors that can be utilized for interhospital comparisons across all areas of the country. WIPSAS=PSrep[1+(PSUSAWIPPSSX)/100]

where PSrep=patient satisfaction reported score, PSUSA=mean reported score for United States (71.84), and WIPPSSX=WIPPSS for individual hospital.

Application of Data Analysis

PPSF, WIPPSS, and WIPSAS were calculated for all HCAHPS‐participating hospitals and compared with averaged raw HCAHPS scores across the United States. WIPSAS and raw scores were specifically analyzed for New York State to demonstrate exactly how adjustments would change state‐level rankings.

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems by County and Hospital Demographics
 Average Positive ScoresNurse CommunicationDoctor CommunicationHelpPainExplain MedsCleanQuietDischarge ExplainRecommend 9/10Definitely Recommend
  • NOTE: Linear regression modeling results of 10 dimensions of patient satisfaction (ie, Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]) and Average Positive Scores (top row) by county demographics and hospital size (left column) are shown. Adjusted R2 (last row) is used to signify the goodness of fit. All models are statistically significant with P=<0.001. Partial regression coefficients () are used to positively or negatively assess contribution to the individual models (ie, each column). The dash () indicates nonsignificance and the asterisk (*) indicates a value that was statistically significant in univariate analysis but not in multivariate analysis. Independent variables (first column) are ordered from top to bottom by the number of HCAHPS dimensions that each contributes to HCAHPS predictive scoring.

Educationalbachelor's0.270.190.450.100.100.050.080.330.150.270.416
Hospital beds0.210.160.190.260.160.170.270.260.060.11 
Population density 20100.090.070.280.200.080.230.140.190.220.07*
White alone percent0.240.250.090.160.230.070.16 0.170.310.317
Total females percent0.110.050.060.070.060.030.050.090.120.09 
African American alone0.190.19 0.090.230.090.070.34*0.090.084
Average travel time to work0.090.10*0.090.060.040.08*0.120.170.16
Foreign‐born percent*0.160.140.060.120.080.060.130.18**
Average household size0.110.050.150.07*0.07*0.01*0.070.076
NonEnglish speaking0.140.120.500.07*****0.340.28
Educationhigh school0.090.090.40*   0.270.060.08*
Household income0.06*0.350.08**0.160.41  0.265
Population 65 years and over*0.140.140.12*0.110.15  *0.10
White, not Hispanic/Latino**0.20***0.090.130.090.220.25
Population under 180.21 0.15 0.08   0.110.20 
Population (county)*0.060.08*0.030.05**0.06**
All ages in poverty  0.24   0.100.220.08*0.281
1 year at same residence*0.130.120.11  0.10*0.04**
Per capita income*0.07*****0.09  *
Population percent change******0.05  **
Adjusted R20.220.250.300.300.120.170.230.300.190.140.15

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R2 of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R2) (Table 2). The best‐fit dimensions were help (adjusted R2=0.304), quiet (adjusted R2=0.299), doctor communication (adjusted R2=0.298), nurse communication (adjusted R2=0.245), and clean (adjusted R2=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R2=0.124), overall 9/10 (adjusted R2=0.136), definitely recommend (adjusted R2=0.150), and explained meds (adjusted R2=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R2=0.155) was closest to the overall model's coefficient of determination (adjusted R2=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R2=0.051), the 2 greatest negative and positive predictors (adjusted R2=0.098), the 4 greatest overall predictors (adjusted R2=0.117), and the 8 greatest overall predictors (adjusted R2=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

Top Ten Highest‐Ranked Hospitals in New York State by HCAHPS Scores Compared to WIPSAS
Ten Highest Ranked New York State Hospitals by HCAHPSTen Highest Ranked New York State Hospitals After WIPSAS
  • NOTE: Top 10 highest‐ranked hospitals in New York State by overall patient satisfaction out of 167 evaluable hospitals are shown. The left column represents the current top 10 hospitals in 2013 by HCAHPS overall patient satisfaction scores, and the right column represents the top 10 hospitals after the WIPSAS adjustment. The 4 factors used to create the WIPSAS adjustment were the 2 most positive partial regression coefficients (educationbachelor's degree, white alone percent 2012) and the 2 most negative partial regression coefficients (number of hospital beds, nonEnglish speaking at home). Three urban academic medical centers, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were reranked from the 46th, 43rd, and 42nd respectively into the top 10. Abbreviations: HCAHPS, Hospital Consumer Assessment of Healthcare Providers and Systems; WIPSAS, Weighted Individual (hospital) Patient Satisfaction Adjustment Score.

1. River Hospital, Inc.1. River Hospital, Inc.
2. Westfield Memorial Hospital, Inc.2. Westfield Memorial Hospital, Inc.
3. Clifton Fine Hospital3. Clifton Fine Hospital
4. Hospital For Special Surgery4. Hospital For Special Surgery
5. Delaware Valley Hospital, Inc.5. New YorkPresbyterian Hospital
6. Putnam Hospital Center6. Delaware Valley Hospital, Inc.
7. Margaretville Memorial Hospital7. Montefiore Medical Center
8. Community Memorial Hospital, Inc.8. St. Francis Hospital, Roslyn
9. Lewis County General Hospital9. Putnam Hospital Center
10. St. Francis Hospital, Roslyn10. Mount Sinai Hospital

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.

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References
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The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1
Overall patient satisfaction by population density decile. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores are segregated by population density deciles (representing 33 million people each). Population density increases along the grey scale. The composite score and 9 out of 10 HCAHPS dimensions demonstrate lower patient satisfaction as population density increases (darker shade). Abbreviations: Doc, doctor; Def Rec, definitely recommend.
Figure 2
Averaged Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores by state correlated with state population (Pop) density. Bivariate correlation of composite HCAHPS scores predicted by state population density without District of Columbia, r = −0.479, P < 0.001 (2‐tailed). This observed correlation informed the hypothesis that population density could predict for lower patient satisfaction via HCAHPS scores.

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

Data Collection

Publically available data were obtained from Hospital Compare,[20] American Hospital Directory,[21] and the US Census Bureau[22] websites. Twenty relevant US Census data categories were selected by their relevance for this study out of the 50 publically reported US Census categories, and included the following: county population, county population density, percent of population change over 1 year, poverty level (percent), income level per capita, median household income, average household size, travel time to work, percentage of high school or college graduates, non‐English primary language spoken at home, percentage of residents born outside of the United States, population percent in same residence for over 1 year, gender, race (white alone, white alone (not Hispanic or Latino), black or African American alone), population over 65 years old, and population under 18 years old.

HCAHPS Development

The HCAHPS survey is 32 questions in length, comprised of 10 evaluative dimensions. All short‐term, acute care, nonspecialty hospitals are invited to participate in the HCAHPS survey.

Data Analysis

Statistical analyses used the Statistical Package for Social Sciences version 16.0 for Windows (SPSS Inc., Chicago, IL). Data were checked for statistical assumptions, including normality, linearity of relationships, and full range of scores. Categories in both the Hospital Compare (HCAHPS) and US Census datasets were analyzed to assess their distribution curves. The category of population densities (per county) was converted to a logarithmic scale to account for a skewed distribution and long tail in the area of low population density. Data were subsequently merged into an Excel (Microsoft, Redmond, WA) spreadsheet using the VLookup function such that relevant 2010 census county data were added to each hospital's Hospital Compare data. Linear regression modeling was performed. Bivariate analysis was conducted (ENTER method) to determine the significant US Census data predictors for each of the 10 Hospital Compare dimensions including the composite overall satisfaction score. Significant predictors were then analyzed in a multivariate model (BACKWORDS method) for each Hospital Compare dimension and the composite average positive score. Models were assessed by determinates of correlation (adjusted R2) to assess for goodness of fit. Statistically significant predictor variables for overall patient satisfaction scores were then ranked according to their partial regression coefficients (standardized ).

A patient satisfaction predictive model was sought based upon significant predictors of aggregate percent positive HCAHPS scores. Various predictor combinations were formed based on their partial coefficients (ie, standardized coefficients); combinations were assessed based on their R2 values and assessed for colinearity. Combinations of partial coefficients included the 2, 4, and 8 most predictive variables as well the 2 most positive and negative predictors. They were then incorporated into a multivariate analysis model (FORWARD method) and assessed based on their adjusted R2 values. A 4‐variable combination (the 2 most predictive positive partial coefficients plus the 2 most predictive negative partial coefficients) was selected as a predictive model, and a formula predictive of the composite overall satisfaction score was generated. This formula (predicted patient satisfaction formula [PPSF]) predicts hospital patient satisfaction HCAHPS scores based on the 4 predictive variables for particular county and hospital characteristics. PPSF=KMV+BHB(HB)+BNE(NE)+BE(E)+BW(W)where KMV=coefficient constant (70.9), B=unstandardized coefficient (see Table 1 for values), HB=number of hospital beds, NE=proportion of non‐English speakers, E=education (proportion with bachelor's degree), and W=proportion identified as white race only.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Average Positive Score by County and Hospital Demographics
 BSE tP
  • NOTE: A multivariate linear regression model of statistically significant dimensions of patient satisfaction as determined by Hospital Consumer Assessment of Healthcare Providers and Systems scores is provided. The dependent variable is the composite of average patient satisfaction scores by hospital (3192 hospitals). Predictors (independent variables) were collected from US Census data for counties or county equivalents. All of the listed predictors (first column) are statistically significant. They are placed in order of partial regression coefficient contribution to the model from most positive to most negative contribution. Adjusted R2 (last row) is used to signify the goodness of fit. Abbreviations: , standardized (partial coefficient); B, unstandardized coefficient; P, statistical significance; SE, standard error; t, t statistic.

Educational attainmentbachelor's degree0.1570.0180.278.612<0.001
White alone percent 20120.090.0120.2357.587<0.001
Resident population percent under 18 years0.4040.04440.2099.085<0.001
Black or African American alone percent 20120.0830.0140.1915.936<0.001
Median household income 200720110.000030.000.0622.0270.043
Population density (log) 20100.2770.0830.0873.33330.001
Average travel time to work0.1070.0240.0884.366<0.001
Educational attainmenthigh school0.0820.0260.0883.1470.002
Average household size2.580.7270.1073.55<0.001
Total females percent 20120.4230.0670.1076.296<0.001
Percent nonEnglish speaking at home 200720110.0520.0180.142.9290.003
No. of hospital beds0.0060.000.21312.901<0.001
Adjusted R20.222    

The PPSF was then modified by weighting with the partial coefficient () to remove the bias in patient satisfaction generated by demographic and structural factors over which individual hospitals have limited or no control. This formula generated a Weighted Individual (hospital) Predicted Patient Satisfaction Score (WIPPSS). Application of this formula narrowed the predicted distribution of patient satisfaction for all hospitals across the country. WIPPSS=KMV+BHB(HB)(1HB)+BNE(NE)(1NE)+BE(E)(1E)+BW(W)(1W)where =standardized coefficient (see Table 1 for values).

To create an adjusted score with direct relevance to the reported patient satisfaction scores, the reported scores were multiplied by an adjustment factor that defines the difference between individual hospital‐weighted scores and the national mean HCAHPS score across the United States. This formula, the Weighted Individual (hospital) Patient Satisfaction Adjustment Score (WIPSAS), represents a patient satisfaction score adjusted for demographic and structural factors that can be utilized for interhospital comparisons across all areas of the country. WIPSAS=PSrep[1+(PSUSAWIPPSSX)/100]

where PSrep=patient satisfaction reported score, PSUSA=mean reported score for United States (71.84), and WIPPSSX=WIPPSS for individual hospital.

Application of Data Analysis

PPSF, WIPPSS, and WIPSAS were calculated for all HCAHPS‐participating hospitals and compared with averaged raw HCAHPS scores across the United States. WIPSAS and raw scores were specifically analyzed for New York State to demonstrate exactly how adjustments would change state‐level rankings.

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems by County and Hospital Demographics
 Average Positive ScoresNurse CommunicationDoctor CommunicationHelpPainExplain MedsCleanQuietDischarge ExplainRecommend 9/10Definitely Recommend
  • NOTE: Linear regression modeling results of 10 dimensions of patient satisfaction (ie, Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]) and Average Positive Scores (top row) by county demographics and hospital size (left column) are shown. Adjusted R2 (last row) is used to signify the goodness of fit. All models are statistically significant with P=<0.001. Partial regression coefficients () are used to positively or negatively assess contribution to the individual models (ie, each column). The dash () indicates nonsignificance and the asterisk (*) indicates a value that was statistically significant in univariate analysis but not in multivariate analysis. Independent variables (first column) are ordered from top to bottom by the number of HCAHPS dimensions that each contributes to HCAHPS predictive scoring.

Educationalbachelor's0.270.190.450.100.100.050.080.330.150.270.416
Hospital beds0.210.160.190.260.160.170.270.260.060.11 
Population density 20100.090.070.280.200.080.230.140.190.220.07*
White alone percent0.240.250.090.160.230.070.16 0.170.310.317
Total females percent0.110.050.060.070.060.030.050.090.120.09 
African American alone0.190.19 0.090.230.090.070.34*0.090.084
Average travel time to work0.090.10*0.090.060.040.08*0.120.170.16
Foreign‐born percent*0.160.140.060.120.080.060.130.18**
Average household size0.110.050.150.07*0.07*0.01*0.070.076
NonEnglish speaking0.140.120.500.07*****0.340.28
Educationhigh school0.090.090.40*   0.270.060.08*
Household income0.06*0.350.08**0.160.41  0.265
Population 65 years and over*0.140.140.12*0.110.15  *0.10
White, not Hispanic/Latino**0.20***0.090.130.090.220.25
Population under 180.21 0.15 0.08   0.110.20 
Population (county)*0.060.08*0.030.05**0.06**
All ages in poverty  0.24   0.100.220.08*0.281
1 year at same residence*0.130.120.11  0.10*0.04**
Per capita income*0.07*****0.09  *
Population percent change******0.05  **
Adjusted R20.220.250.300.300.120.170.230.300.190.140.15

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R2 of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R2) (Table 2). The best‐fit dimensions were help (adjusted R2=0.304), quiet (adjusted R2=0.299), doctor communication (adjusted R2=0.298), nurse communication (adjusted R2=0.245), and clean (adjusted R2=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R2=0.124), overall 9/10 (adjusted R2=0.136), definitely recommend (adjusted R2=0.150), and explained meds (adjusted R2=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R2=0.155) was closest to the overall model's coefficient of determination (adjusted R2=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R2=0.051), the 2 greatest negative and positive predictors (adjusted R2=0.098), the 4 greatest overall predictors (adjusted R2=0.117), and the 8 greatest overall predictors (adjusted R2=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

Top Ten Highest‐Ranked Hospitals in New York State by HCAHPS Scores Compared to WIPSAS
Ten Highest Ranked New York State Hospitals by HCAHPSTen Highest Ranked New York State Hospitals After WIPSAS
  • NOTE: Top 10 highest‐ranked hospitals in New York State by overall patient satisfaction out of 167 evaluable hospitals are shown. The left column represents the current top 10 hospitals in 2013 by HCAHPS overall patient satisfaction scores, and the right column represents the top 10 hospitals after the WIPSAS adjustment. The 4 factors used to create the WIPSAS adjustment were the 2 most positive partial regression coefficients (educationbachelor's degree, white alone percent 2012) and the 2 most negative partial regression coefficients (number of hospital beds, nonEnglish speaking at home). Three urban academic medical centers, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were reranked from the 46th, 43rd, and 42nd respectively into the top 10. Abbreviations: HCAHPS, Hospital Consumer Assessment of Healthcare Providers and Systems; WIPSAS, Weighted Individual (hospital) Patient Satisfaction Adjustment Score.

1. River Hospital, Inc.1. River Hospital, Inc.
2. Westfield Memorial Hospital, Inc.2. Westfield Memorial Hospital, Inc.
3. Clifton Fine Hospital3. Clifton Fine Hospital
4. Hospital For Special Surgery4. Hospital For Special Surgery
5. Delaware Valley Hospital, Inc.5. New YorkPresbyterian Hospital
6. Putnam Hospital Center6. Delaware Valley Hospital, Inc.
7. Margaretville Memorial Hospital7. Montefiore Medical Center
8. Community Memorial Hospital, Inc.8. St. Francis Hospital, Roslyn
9. Lewis County General Hospital9. Putnam Hospital Center
10. St. Francis Hospital, Roslyn10. Mount Sinai Hospital

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.

The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1
Overall patient satisfaction by population density decile. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores are segregated by population density deciles (representing 33 million people each). Population density increases along the grey scale. The composite score and 9 out of 10 HCAHPS dimensions demonstrate lower patient satisfaction as population density increases (darker shade). Abbreviations: Doc, doctor; Def Rec, definitely recommend.
Figure 2
Averaged Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores by state correlated with state population (Pop) density. Bivariate correlation of composite HCAHPS scores predicted by state population density without District of Columbia, r = −0.479, P < 0.001 (2‐tailed). This observed correlation informed the hypothesis that population density could predict for lower patient satisfaction via HCAHPS scores.

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

Data Collection

Publically available data were obtained from Hospital Compare,[20] American Hospital Directory,[21] and the US Census Bureau[22] websites. Twenty relevant US Census data categories were selected by their relevance for this study out of the 50 publically reported US Census categories, and included the following: county population, county population density, percent of population change over 1 year, poverty level (percent), income level per capita, median household income, average household size, travel time to work, percentage of high school or college graduates, non‐English primary language spoken at home, percentage of residents born outside of the United States, population percent in same residence for over 1 year, gender, race (white alone, white alone (not Hispanic or Latino), black or African American alone), population over 65 years old, and population under 18 years old.

HCAHPS Development

The HCAHPS survey is 32 questions in length, comprised of 10 evaluative dimensions. All short‐term, acute care, nonspecialty hospitals are invited to participate in the HCAHPS survey.

Data Analysis

Statistical analyses used the Statistical Package for Social Sciences version 16.0 for Windows (SPSS Inc., Chicago, IL). Data were checked for statistical assumptions, including normality, linearity of relationships, and full range of scores. Categories in both the Hospital Compare (HCAHPS) and US Census datasets were analyzed to assess their distribution curves. The category of population densities (per county) was converted to a logarithmic scale to account for a skewed distribution and long tail in the area of low population density. Data were subsequently merged into an Excel (Microsoft, Redmond, WA) spreadsheet using the VLookup function such that relevant 2010 census county data were added to each hospital's Hospital Compare data. Linear regression modeling was performed. Bivariate analysis was conducted (ENTER method) to determine the significant US Census data predictors for each of the 10 Hospital Compare dimensions including the composite overall satisfaction score. Significant predictors were then analyzed in a multivariate model (BACKWORDS method) for each Hospital Compare dimension and the composite average positive score. Models were assessed by determinates of correlation (adjusted R2) to assess for goodness of fit. Statistically significant predictor variables for overall patient satisfaction scores were then ranked according to their partial regression coefficients (standardized ).

A patient satisfaction predictive model was sought based upon significant predictors of aggregate percent positive HCAHPS scores. Various predictor combinations were formed based on their partial coefficients (ie, standardized coefficients); combinations were assessed based on their R2 values and assessed for colinearity. Combinations of partial coefficients included the 2, 4, and 8 most predictive variables as well the 2 most positive and negative predictors. They were then incorporated into a multivariate analysis model (FORWARD method) and assessed based on their adjusted R2 values. A 4‐variable combination (the 2 most predictive positive partial coefficients plus the 2 most predictive negative partial coefficients) was selected as a predictive model, and a formula predictive of the composite overall satisfaction score was generated. This formula (predicted patient satisfaction formula [PPSF]) predicts hospital patient satisfaction HCAHPS scores based on the 4 predictive variables for particular county and hospital characteristics. PPSF=KMV+BHB(HB)+BNE(NE)+BE(E)+BW(W)where KMV=coefficient constant (70.9), B=unstandardized coefficient (see Table 1 for values), HB=number of hospital beds, NE=proportion of non‐English speakers, E=education (proportion with bachelor's degree), and W=proportion identified as white race only.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Average Positive Score by County and Hospital Demographics
 BSE tP
  • NOTE: A multivariate linear regression model of statistically significant dimensions of patient satisfaction as determined by Hospital Consumer Assessment of Healthcare Providers and Systems scores is provided. The dependent variable is the composite of average patient satisfaction scores by hospital (3192 hospitals). Predictors (independent variables) were collected from US Census data for counties or county equivalents. All of the listed predictors (first column) are statistically significant. They are placed in order of partial regression coefficient contribution to the model from most positive to most negative contribution. Adjusted R2 (last row) is used to signify the goodness of fit. Abbreviations: , standardized (partial coefficient); B, unstandardized coefficient; P, statistical significance; SE, standard error; t, t statistic.

Educational attainmentbachelor's degree0.1570.0180.278.612<0.001
White alone percent 20120.090.0120.2357.587<0.001
Resident population percent under 18 years0.4040.04440.2099.085<0.001
Black or African American alone percent 20120.0830.0140.1915.936<0.001
Median household income 200720110.000030.000.0622.0270.043
Population density (log) 20100.2770.0830.0873.33330.001
Average travel time to work0.1070.0240.0884.366<0.001
Educational attainmenthigh school0.0820.0260.0883.1470.002
Average household size2.580.7270.1073.55<0.001
Total females percent 20120.4230.0670.1076.296<0.001
Percent nonEnglish speaking at home 200720110.0520.0180.142.9290.003
No. of hospital beds0.0060.000.21312.901<0.001
Adjusted R20.222    

The PPSF was then modified by weighting with the partial coefficient () to remove the bias in patient satisfaction generated by demographic and structural factors over which individual hospitals have limited or no control. This formula generated a Weighted Individual (hospital) Predicted Patient Satisfaction Score (WIPPSS). Application of this formula narrowed the predicted distribution of patient satisfaction for all hospitals across the country. WIPPSS=KMV+BHB(HB)(1HB)+BNE(NE)(1NE)+BE(E)(1E)+BW(W)(1W)where =standardized coefficient (see Table 1 for values).

To create an adjusted score with direct relevance to the reported patient satisfaction scores, the reported scores were multiplied by an adjustment factor that defines the difference between individual hospital‐weighted scores and the national mean HCAHPS score across the United States. This formula, the Weighted Individual (hospital) Patient Satisfaction Adjustment Score (WIPSAS), represents a patient satisfaction score adjusted for demographic and structural factors that can be utilized for interhospital comparisons across all areas of the country. WIPSAS=PSrep[1+(PSUSAWIPPSSX)/100]

where PSrep=patient satisfaction reported score, PSUSA=mean reported score for United States (71.84), and WIPPSSX=WIPPSS for individual hospital.

Application of Data Analysis

PPSF, WIPPSS, and WIPSAS were calculated for all HCAHPS‐participating hospitals and compared with averaged raw HCAHPS scores across the United States. WIPSAS and raw scores were specifically analyzed for New York State to demonstrate exactly how adjustments would change state‐level rankings.

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems by County and Hospital Demographics
 Average Positive ScoresNurse CommunicationDoctor CommunicationHelpPainExplain MedsCleanQuietDischarge ExplainRecommend 9/10Definitely Recommend
  • NOTE: Linear regression modeling results of 10 dimensions of patient satisfaction (ie, Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]) and Average Positive Scores (top row) by county demographics and hospital size (left column) are shown. Adjusted R2 (last row) is used to signify the goodness of fit. All models are statistically significant with P=<0.001. Partial regression coefficients () are used to positively or negatively assess contribution to the individual models (ie, each column). The dash () indicates nonsignificance and the asterisk (*) indicates a value that was statistically significant in univariate analysis but not in multivariate analysis. Independent variables (first column) are ordered from top to bottom by the number of HCAHPS dimensions that each contributes to HCAHPS predictive scoring.

Educationalbachelor's0.270.190.450.100.100.050.080.330.150.270.416
Hospital beds0.210.160.190.260.160.170.270.260.060.11 
Population density 20100.090.070.280.200.080.230.140.190.220.07*
White alone percent0.240.250.090.160.230.070.16 0.170.310.317
Total females percent0.110.050.060.070.060.030.050.090.120.09 
African American alone0.190.19 0.090.230.090.070.34*0.090.084
Average travel time to work0.090.10*0.090.060.040.08*0.120.170.16
Foreign‐born percent*0.160.140.060.120.080.060.130.18**
Average household size0.110.050.150.07*0.07*0.01*0.070.076
NonEnglish speaking0.140.120.500.07*****0.340.28
Educationhigh school0.090.090.40*   0.270.060.08*
Household income0.06*0.350.08**0.160.41  0.265
Population 65 years and over*0.140.140.12*0.110.15  *0.10
White, not Hispanic/Latino**0.20***0.090.130.090.220.25
Population under 180.21 0.15 0.08   0.110.20 
Population (county)*0.060.08*0.030.05**0.06**
All ages in poverty  0.24   0.100.220.08*0.281
1 year at same residence*0.130.120.11  0.10*0.04**
Per capita income*0.07*****0.09  *
Population percent change******0.05  **
Adjusted R20.220.250.300.300.120.170.230.300.190.140.15

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R2 of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R2) (Table 2). The best‐fit dimensions were help (adjusted R2=0.304), quiet (adjusted R2=0.299), doctor communication (adjusted R2=0.298), nurse communication (adjusted R2=0.245), and clean (adjusted R2=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R2=0.124), overall 9/10 (adjusted R2=0.136), definitely recommend (adjusted R2=0.150), and explained meds (adjusted R2=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R2=0.155) was closest to the overall model's coefficient of determination (adjusted R2=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R2=0.051), the 2 greatest negative and positive predictors (adjusted R2=0.098), the 4 greatest overall predictors (adjusted R2=0.117), and the 8 greatest overall predictors (adjusted R2=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

Top Ten Highest‐Ranked Hospitals in New York State by HCAHPS Scores Compared to WIPSAS
Ten Highest Ranked New York State Hospitals by HCAHPSTen Highest Ranked New York State Hospitals After WIPSAS
  • NOTE: Top 10 highest‐ranked hospitals in New York State by overall patient satisfaction out of 167 evaluable hospitals are shown. The left column represents the current top 10 hospitals in 2013 by HCAHPS overall patient satisfaction scores, and the right column represents the top 10 hospitals after the WIPSAS adjustment. The 4 factors used to create the WIPSAS adjustment were the 2 most positive partial regression coefficients (educationbachelor's degree, white alone percent 2012) and the 2 most negative partial regression coefficients (number of hospital beds, nonEnglish speaking at home). Three urban academic medical centers, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were reranked from the 46th, 43rd, and 42nd respectively into the top 10. Abbreviations: HCAHPS, Hospital Consumer Assessment of Healthcare Providers and Systems; WIPSAS, Weighted Individual (hospital) Patient Satisfaction Adjustment Score.

1. River Hospital, Inc.1. River Hospital, Inc.
2. Westfield Memorial Hospital, Inc.2. Westfield Memorial Hospital, Inc.
3. Clifton Fine Hospital3. Clifton Fine Hospital
4. Hospital For Special Surgery4. Hospital For Special Surgery
5. Delaware Valley Hospital, Inc.5. New YorkPresbyterian Hospital
6. Putnam Hospital Center6. Delaware Valley Hospital, Inc.
7. Margaretville Memorial Hospital7. Montefiore Medical Center
8. Community Memorial Hospital, Inc.8. St. Francis Hospital, Roslyn
9. Lewis County General Hospital9. Putnam Hospital Center
10. St. Francis Hospital, Roslyn10. Mount Sinai Hospital

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.

References
  1. Florence CS, Atherly A, Thorpe KE. Will choice‐based reform work for Medicare? Evidence from the Federal Employees Health Benefits Program. Health Serv Res. 2006;41:17411761.
  2. H.R. 3590. Patient Protection and Affordable Care Act 2010 (2010).
  3. Donabedian A. The quality of care. How can it be assessed? JAMA. 1988;260(12):17431748.
  4. Lake Superior Quality Innovation Network. FY 2017 Value‐Based Purchasing domain weighting. Available at: http://www.stratishealth.org/documents/VBP‐FY2017.pdf. Accessed March 13, 2015.
  5. Hospital Value‐Based Purchasing Program. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/Hospital‐Value‐Based‐Purchasing. Accessed December 1st, 2013.
  6. Jha AK, Orav EJ, Zheng J, Epstein AM. Patients' perception of hospital care in the United States. N Engl J Med. 2008;359(18):19211931.
  7. Porter ME, Lee TH. Providers must lead the way in making value the overarching goal Harvard Bus Rev. October 2013:319.
  8. Jha AK, Orav EJ, Epstein AM. The effect of financial incentives on hospitals that serve poor patients. Ann Intern Med. 2010;153(5):299306.
  9. Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA. 2013;309(4):342343.
  10. Ryan AM. Will value‐based purchasing increase disparities in care? N Engl J Med. 2013;369(26):24722474.
  11. Thorpe KE, Florence CS, Seiber EE. Hospital conversions, margins, and the provision of uncompensated care. Health Aff (Millwood). 2000;19(6):187194.
  12. Borah BJ, Rock MG, Wood DL, Roellinger DL, Johnson MG, Naessens JM. Association between value‐based purchasing score and hospital characteristics. BMC Health Serv Res. 2012;12:464.
  13. Elliott MN, Zaslavsky AM, Goldstein E, et al. Effects of survey mode, patient mix, and nonresponse on CAHPS hospital survey scores. Health Serv Res. 2009;44(2 pt 1):501518.
  14. Burroughs TE, Waterman BM, Cira JC, Desikan R, Claiborne Dunagan W. Patient satisfaction measurement strategies: a comparison of phone and mail methods. Jt Comm J Qual Improv. 2001;27(7):349361.
  15. Fowler FJ, Gallagher PM, Nederend S. Comparing telephone and mail responses to the CAHPS survey instrument. Consumer Assessment of Health Plans Study. Med Care. 1999;37(3 suppl):MS41MS49.
  16. Rodriguez HP, Glahn T, Rogers WH, Chang H, Fanjiang G, Safran DG. Evaluating patients' experiences with individual physicians: a randomized trial of mail, internet, and interactive voice response telephone administration of surveys. Med Care. 2006;44(2):167174.
  17. O'Malley AJ, Zaslavsky AM, Elliott MN, Zaborski L, Cleary PD. Case‐mix adjustment of the CAHPS Hospital Survey. Health Serv Res. 2005;40(6 pt 2):21622181.
  18. Mode and patient‐mix adjustments of CAHPS hospital survey (HCAHPS). Available at: http://www.hcahpsonline.org/modeadjustment.aspx. Accessed December 1, 2013.
  19. Zaslavsky AM, Zaborski LB, Ding L, Shaul JA, Cioffi MJ, Clear PD. Adjusting performance measures to ensure equitable plan comparisons. Health Care Financ Rev. 2001;22(3):109126.
  20. Official Hospital Compare Data. Displaying datasets in Patient Survey Results category. Available at: https://data.medicare.gov/data/hospital‐compare/Patient%20Survey%20Results. Accessed December 1, 2013.
  21. Hospital statistics by state. American Hospital Directory, Inc. website. Available at: http://www.ahd.com/state_statistics.html. Accessed December 1, 2013.
  22. U.S. Census Download Center. Available at: http://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml. Accessed December 1, 2013.
References
  1. Florence CS, Atherly A, Thorpe KE. Will choice‐based reform work for Medicare? Evidence from the Federal Employees Health Benefits Program. Health Serv Res. 2006;41:17411761.
  2. H.R. 3590. Patient Protection and Affordable Care Act 2010 (2010).
  3. Donabedian A. The quality of care. How can it be assessed? JAMA. 1988;260(12):17431748.
  4. Lake Superior Quality Innovation Network. FY 2017 Value‐Based Purchasing domain weighting. Available at: http://www.stratishealth.org/documents/VBP‐FY2017.pdf. Accessed March 13, 2015.
  5. Hospital Value‐Based Purchasing Program. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/Hospital‐Value‐Based‐Purchasing. Accessed December 1st, 2013.
  6. Jha AK, Orav EJ, Zheng J, Epstein AM. Patients' perception of hospital care in the United States. N Engl J Med. 2008;359(18):19211931.
  7. Porter ME, Lee TH. Providers must lead the way in making value the overarching goal Harvard Bus Rev. October 2013:319.
  8. Jha AK, Orav EJ, Epstein AM. The effect of financial incentives on hospitals that serve poor patients. Ann Intern Med. 2010;153(5):299306.
  9. Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA. 2013;309(4):342343.
  10. Ryan AM. Will value‐based purchasing increase disparities in care? N Engl J Med. 2013;369(26):24722474.
  11. Thorpe KE, Florence CS, Seiber EE. Hospital conversions, margins, and the provision of uncompensated care. Health Aff (Millwood). 2000;19(6):187194.
  12. Borah BJ, Rock MG, Wood DL, Roellinger DL, Johnson MG, Naessens JM. Association between value‐based purchasing score and hospital characteristics. BMC Health Serv Res. 2012;12:464.
  13. Elliott MN, Zaslavsky AM, Goldstein E, et al. Effects of survey mode, patient mix, and nonresponse on CAHPS hospital survey scores. Health Serv Res. 2009;44(2 pt 1):501518.
  14. Burroughs TE, Waterman BM, Cira JC, Desikan R, Claiborne Dunagan W. Patient satisfaction measurement strategies: a comparison of phone and mail methods. Jt Comm J Qual Improv. 2001;27(7):349361.
  15. Fowler FJ, Gallagher PM, Nederend S. Comparing telephone and mail responses to the CAHPS survey instrument. Consumer Assessment of Health Plans Study. Med Care. 1999;37(3 suppl):MS41MS49.
  16. Rodriguez HP, Glahn T, Rogers WH, Chang H, Fanjiang G, Safran DG. Evaluating patients' experiences with individual physicians: a randomized trial of mail, internet, and interactive voice response telephone administration of surveys. Med Care. 2006;44(2):167174.
  17. O'Malley AJ, Zaslavsky AM, Elliott MN, Zaborski L, Cleary PD. Case‐mix adjustment of the CAHPS Hospital Survey. Health Serv Res. 2005;40(6 pt 2):21622181.
  18. Mode and patient‐mix adjustments of CAHPS hospital survey (HCAHPS). Available at: http://www.hcahpsonline.org/modeadjustment.aspx. Accessed December 1, 2013.
  19. Zaslavsky AM, Zaborski LB, Ding L, Shaul JA, Cioffi MJ, Clear PD. Adjusting performance measures to ensure equitable plan comparisons. Health Care Financ Rev. 2001;22(3):109126.
  20. Official Hospital Compare Data. Displaying datasets in Patient Survey Results category. Available at: https://data.medicare.gov/data/hospital‐compare/Patient%20Survey%20Results. Accessed December 1, 2013.
  21. Hospital statistics by state. American Hospital Directory, Inc. website. Available at: http://www.ahd.com/state_statistics.html. Accessed December 1, 2013.
  22. U.S. Census Download Center. Available at: http://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml. Accessed December 1, 2013.
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Address for correspondence and reprint requests: Daniel McFarland, DO, Hematology/Oncology, Mount Sinai Medical Center, One Gustave L. Levy Place, Box 1079, New York, NY 10029; Telephone: 212–659‐5420; Fax: 212–241‐2684; E‐mail: [email protected]
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Nurse Case Management Fails to Yield Improvements in Blood Pressure and Glycemic Control

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Nurse Case Management Fails to Yield Improvements in Blood Pressure and Glycemic Control

Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

References

1. Crowley MJ, Bosworth HB, Coffman CJ, et al. Tailored Case Management for Diabetes and Hypertension (TEACH-DM) in a community population: study design and baseline sample characteristics. Contemp Clin Trials 2013;36:298–306.

2. Aubert RE, Herman WH, Waters J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. A randomized, controlled trial. Ann Intern Med 1998;129:605–12.

3. Thompson DM, Kozak SE, Sheps S. Insulin adjustment by a diabetes nurse educator improves glucose control in insulin-requiring diabetic patients: a randomized trial. CMAJ 1999;161:959–62.

4. Weinberger M, Kirkman MS, Samsa GP, et al. A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med 1995;10:59–66.

5. Bosworth HB, Powers BJ, Olsen MK, et al. Home blood pressure management and improved blood pressure control: results from a randomized controlled trial. Arch Intern Med 2011;171:1173–80.

6. Bosworth HB, Olsen MK, Grubber JM, et al. Two self-management interventions to improve hypertension control: a randomized trial. Ann Intern Med 2009;151:687–95.

7. CDC Diabetes Cost-effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 2002;287:2542–51.

8. Mons U, Raum E, Krämer HU, et al. Effectiveness of a supportive telephone counseling intervention in type 2 diabetes patients: randomized controlled study. PLoS One 2013;8:e77954.

9. Blackberry ID, Furler JS, Best JD, et al. Effectiveness of general practice based, practice nurse led telephone coaching on glycaemic control of type 2 diabetes: the Patient Engagement and Coaching for Health (PEACH) pragmatic cluster randomised controlled trial. BMJ 2013;347:f5272.

10. Eakin EG, Lawler SP, Vandelanotte C, Owen N. Telephone interventions for physical activity and dietary behavior change: a systematic review. Am J Prev Med 2007;32:419–34.

11. Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA 2006;296:427–40.

12. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med 2010;363:2611–20.

13. Walsh JL, Harris BHL, Roberts AW. Evaluation of a community diabetes initiative: Integrating diabetes care. Prim Care Diabetes 2014 Dec 11.

14. Halme L, Vesalainen R, Kaaja M, Kantola I. Self-monitoring of blood pressure promotes achievement of blood pressure target in primary health care. Am J Hypertens 2005;18:1415–20.

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Journal of Clinical Outcomes Management - May 2015, VOL. 22, NO. 5
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Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

References

1. Crowley MJ, Bosworth HB, Coffman CJ, et al. Tailored Case Management for Diabetes and Hypertension (TEACH-DM) in a community population: study design and baseline sample characteristics. Contemp Clin Trials 2013;36:298–306.

2. Aubert RE, Herman WH, Waters J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. A randomized, controlled trial. Ann Intern Med 1998;129:605–12.

3. Thompson DM, Kozak SE, Sheps S. Insulin adjustment by a diabetes nurse educator improves glucose control in insulin-requiring diabetic patients: a randomized trial. CMAJ 1999;161:959–62.

4. Weinberger M, Kirkman MS, Samsa GP, et al. A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med 1995;10:59–66.

5. Bosworth HB, Powers BJ, Olsen MK, et al. Home blood pressure management and improved blood pressure control: results from a randomized controlled trial. Arch Intern Med 2011;171:1173–80.

6. Bosworth HB, Olsen MK, Grubber JM, et al. Two self-management interventions to improve hypertension control: a randomized trial. Ann Intern Med 2009;151:687–95.

7. CDC Diabetes Cost-effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 2002;287:2542–51.

8. Mons U, Raum E, Krämer HU, et al. Effectiveness of a supportive telephone counseling intervention in type 2 diabetes patients: randomized controlled study. PLoS One 2013;8:e77954.

9. Blackberry ID, Furler JS, Best JD, et al. Effectiveness of general practice based, practice nurse led telephone coaching on glycaemic control of type 2 diabetes: the Patient Engagement and Coaching for Health (PEACH) pragmatic cluster randomised controlled trial. BMJ 2013;347:f5272.

10. Eakin EG, Lawler SP, Vandelanotte C, Owen N. Telephone interventions for physical activity and dietary behavior change: a systematic review. Am J Prev Med 2007;32:419–34.

11. Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA 2006;296:427–40.

12. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med 2010;363:2611–20.

13. Walsh JL, Harris BHL, Roberts AW. Evaluation of a community diabetes initiative: Integrating diabetes care. Prim Care Diabetes 2014 Dec 11.

14. Halme L, Vesalainen R, Kaaja M, Kantola I. Self-monitoring of blood pressure promotes achievement of blood pressure target in primary health care. Am J Hypertens 2005;18:1415–20.

References

1. Crowley MJ, Bosworth HB, Coffman CJ, et al. Tailored Case Management for Diabetes and Hypertension (TEACH-DM) in a community population: study design and baseline sample characteristics. Contemp Clin Trials 2013;36:298–306.

2. Aubert RE, Herman WH, Waters J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. A randomized, controlled trial. Ann Intern Med 1998;129:605–12.

3. Thompson DM, Kozak SE, Sheps S. Insulin adjustment by a diabetes nurse educator improves glucose control in insulin-requiring diabetic patients: a randomized trial. CMAJ 1999;161:959–62.

4. Weinberger M, Kirkman MS, Samsa GP, et al. A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus: impact on glycemic control and health-related quality of life. J Gen Intern Med 1995;10:59–66.

5. Bosworth HB, Powers BJ, Olsen MK, et al. Home blood pressure management and improved blood pressure control: results from a randomized controlled trial. Arch Intern Med 2011;171:1173–80.

6. Bosworth HB, Olsen MK, Grubber JM, et al. Two self-management interventions to improve hypertension control: a randomized trial. Ann Intern Med 2009;151:687–95.

7. CDC Diabetes Cost-effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 2002;287:2542–51.

8. Mons U, Raum E, Krämer HU, et al. Effectiveness of a supportive telephone counseling intervention in type 2 diabetes patients: randomized controlled study. PLoS One 2013;8:e77954.

9. Blackberry ID, Furler JS, Best JD, et al. Effectiveness of general practice based, practice nurse led telephone coaching on glycaemic control of type 2 diabetes: the Patient Engagement and Coaching for Health (PEACH) pragmatic cluster randomised controlled trial. BMJ 2013;347:f5272.

10. Eakin EG, Lawler SP, Vandelanotte C, Owen N. Telephone interventions for physical activity and dietary behavior change: a systematic review. Am J Prev Med 2007;32:419–34.

11. Shojania KG, Ranji SR, McDonald KM, et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA 2006;296:427–40.

12. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med 2010;363:2611–20.

13. Walsh JL, Harris BHL, Roberts AW. Evaluation of a community diabetes initiative: Integrating diabetes care. Prim Care Diabetes 2014 Dec 11.

14. Halme L, Vesalainen R, Kaaja M, Kantola I. Self-monitoring of blood pressure promotes achievement of blood pressure target in primary health care. Am J Hypertens 2005;18:1415–20.

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Journal of Clinical Outcomes Management - May 2015, VOL. 22, NO. 5
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Which Revascularization Strategy for Multivessel Coronary Disease?

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Which Revascularization Strategy for Multivessel Coronary Disease?

Study Overview

Objective. To compare percutaneous coronary intervention (PCI) using second-generation drug-eluting stents (everolimus-eluting stents) with coronary artery bypass grafting (CABG) among patients with multivessel coronary disease.

Design. Observational registry study with propensity-score matching.

Setting and participants. The study relies on patients identified from the Cardiac Surgery Reporting System (CSRS) and Percutaneous Coronary Intervention Reporting System (PCIRS) registries of the New York State Department of Health. These 2 registries were linked to the New York State Vital Statistics Death registry and to the Statewide Planning and Research Cooperative System registry (SPARCS) to obtain further information like dates of admission, surgery, discharge, and death. Subjects were eligible for inclusion if they had multivessel disease (defined as severe stenosis [≥ 70%] in at least 2 diseased major epicardial coronary arteries) and if they had undergone either PCI with implantation of an everolimus-eluting stent or CABG. Subjects were excluded if they had revascularization within 1 year before index procedure; previous cardiac surgery; severe left main coronary artery disease (degree of stenosis ≥ 50%); PCI with a stent other than an everolimus-eluting stent; myocardial infarction without 24 hours before the index procedure; and unstable hemodynamics or cardiogenic shock.

Main outcome measures. The primary outcome of the study was all-cause mortality. Various secondary outcomes included rates of myocardial infarction, stroke, and repeat vascularization.

Main results. Among 116,915 patients assessed for eligibility, 82,096 were excluded. Among 34,819 who met inclusion criteria, 18,446 were included in the propensity score–matched analysis. With a 1:1 matching algorithm, 9223 were in the PCI with everolimus-eluting stent group and 9223 were in the CABG group. Short-term outcomes (in hospital or ≤ 30 days after the index procedure) favored PCI with everolimus-eluting stents over CABG, with a significantly lower risk of death (0.6% vs. 1.1%; hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.35 to 0.69; P < 0.002) as well as stroke (0.2% vs 1.2%; HR, 0.18; 95% CI, 0.11 to 0.29; P < 0.001). The 2 groups had similar rates of myocardial infarction in the short-term (0.5% and 0.4%; HR, 1.37; 95% CI, 0.89 to 2.12; P = 0.16). After a mean follow-up of 2.9 years, there was a similar annual death rate between groups: 3.1% for PCI and 2.9% for CABG (HR, 1.04; 95% CI, 0.93 to 1.17; P = 0.50). PCI with everolimus-eluting stents was associated with a higher risk of a first myocardial infarction than was CABG (1.9% vs 1.1% per year; HR, 1.51; 95% CI, 1.29 to 1.77; P < 0.001). PCI with everolimus-eluting stents was associated with a lower risk of a first stroke than CABG (0.7% vs. 1.0% per year; HR, 0.62; 95% CI, 0.50 to 0.76; P < 0.001). Finally, PCI with everolimus-eluting stents was associated with a higher risk of a first repeat-revascularization procedure than CABG (7.2% vs. 3.1% per year; HR, 2.35; 95% CI, 2.14 to 2.58; P < 0.001).

Conclusion. In the setting of newer stent technology with second-generation everolimus-eluting stents, the risk of death associated with PCI was similar to that associated with CABG for multivessel coronary artery disease. In the long-term, PCI was associated with a higher risk of myocardial infarction and repeat revascularization, whereas CABG was associated with an increased risk of stroke. In the short-term, PCI had lower risks of both death and stroke.

Commentary

Coronary artery disease is a major public health problem. For patients for whom revascularization is deemed to be appropriate, a choice must be made between PCI and CABG. In previous studies that compared PCI and CABG, CABG was shown to have less need for repeat revascularizations as well as mortality benefits [1–3]. However, these prior studies compared CABG with older generations of stents. In the past decade, stent technologies have improved, as the bare-metal stent era gave way to the first generation of of drug-eluting stents (with sirolimus or paclitaxel), to be followed by second-generation drug-eluting stents (with everolimus or zotarolimus) [4].

In this article, Bangalore and colleagues addressed the issue of whether the use of second-generation drug-eluting stents close the outcome gap that favors CABG over PCI in patients with multivessel coronary artery disease. In patients who were considered to have had complete revascularization performed during PCI (ie, revascularization of all major vessels with clinically significant stenosis), they noted mitigation of the outcome differences between the PCI group and the CABG group. They conclude that the decision-making process by patients and their providers regarding revascularization be placed in the context of individual values and preferences.

One major limitation is that the study is an observational study from registry data. Despite the use of sophisticated statistical techniques including propensity score matching to adjust for confounders that are implicit in any nonrandomized comparison of treatment strategies, observational studies suffer from the definitely proof of causality. These limitations are especially important when the two groups being compared have modest differences in outcome.

Applications for Clinical Practice

This observational study, together with a recent randomized clinical trial in which CABG was compared with PCI with the use of everolimus-eluting stents from the BEST trial [5], provided new insights of the 2 revascularization strategies. Clinicians should engage and empower patients with a shared decision-making approach. The early hazard of CABG in stroke and death may be unacceptable to some patients, whereas others might want to avoid the later hazards of PCI in repeat procedure or having a myocardial infarction. Until a definitive study is available, patients should be informed of the best current knowledge of the pros and cons of the two revascularization strategies.

 —Ka Ming Gordon Ngai, MD, MPH

 

References

1. Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet 2013;381: 639–50.

2. Hannan EL, Racz MJ, Arani DT, et al. A comparison of short- and long-term outcomes for balloon angioplasty and coronary stent placement. J Am Coll Cardiol 2000;36:395–403.

3. Hannan EL, Racz MJ, Walford G, et al. Long-term outcomes of coronary-artery bypass grafting versus stent implantation. N Engl J Med 2005;352: 2174–83.

4. Harrington RA. Selecting revascularization strategies in patients with coronary disease. N Engl J Med 2015;372: 1261–3.

5. Park SJ, Ahn JM, Kim YH, et al. Trial of everolimus-eluting stents or bypass surgery for coronary disease. N Engl J Med 2015;372:1204–12.

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Journal of Clinical Outcomes Management - May 2015, VOL. 22, NO. 5
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Study Overview

Objective. To compare percutaneous coronary intervention (PCI) using second-generation drug-eluting stents (everolimus-eluting stents) with coronary artery bypass grafting (CABG) among patients with multivessel coronary disease.

Design. Observational registry study with propensity-score matching.

Setting and participants. The study relies on patients identified from the Cardiac Surgery Reporting System (CSRS) and Percutaneous Coronary Intervention Reporting System (PCIRS) registries of the New York State Department of Health. These 2 registries were linked to the New York State Vital Statistics Death registry and to the Statewide Planning and Research Cooperative System registry (SPARCS) to obtain further information like dates of admission, surgery, discharge, and death. Subjects were eligible for inclusion if they had multivessel disease (defined as severe stenosis [≥ 70%] in at least 2 diseased major epicardial coronary arteries) and if they had undergone either PCI with implantation of an everolimus-eluting stent or CABG. Subjects were excluded if they had revascularization within 1 year before index procedure; previous cardiac surgery; severe left main coronary artery disease (degree of stenosis ≥ 50%); PCI with a stent other than an everolimus-eluting stent; myocardial infarction without 24 hours before the index procedure; and unstable hemodynamics or cardiogenic shock.

Main outcome measures. The primary outcome of the study was all-cause mortality. Various secondary outcomes included rates of myocardial infarction, stroke, and repeat vascularization.

Main results. Among 116,915 patients assessed for eligibility, 82,096 were excluded. Among 34,819 who met inclusion criteria, 18,446 were included in the propensity score–matched analysis. With a 1:1 matching algorithm, 9223 were in the PCI with everolimus-eluting stent group and 9223 were in the CABG group. Short-term outcomes (in hospital or ≤ 30 days after the index procedure) favored PCI with everolimus-eluting stents over CABG, with a significantly lower risk of death (0.6% vs. 1.1%; hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.35 to 0.69; P < 0.002) as well as stroke (0.2% vs 1.2%; HR, 0.18; 95% CI, 0.11 to 0.29; P < 0.001). The 2 groups had similar rates of myocardial infarction in the short-term (0.5% and 0.4%; HR, 1.37; 95% CI, 0.89 to 2.12; P = 0.16). After a mean follow-up of 2.9 years, there was a similar annual death rate between groups: 3.1% for PCI and 2.9% for CABG (HR, 1.04; 95% CI, 0.93 to 1.17; P = 0.50). PCI with everolimus-eluting stents was associated with a higher risk of a first myocardial infarction than was CABG (1.9% vs 1.1% per year; HR, 1.51; 95% CI, 1.29 to 1.77; P < 0.001). PCI with everolimus-eluting stents was associated with a lower risk of a first stroke than CABG (0.7% vs. 1.0% per year; HR, 0.62; 95% CI, 0.50 to 0.76; P < 0.001). Finally, PCI with everolimus-eluting stents was associated with a higher risk of a first repeat-revascularization procedure than CABG (7.2% vs. 3.1% per year; HR, 2.35; 95% CI, 2.14 to 2.58; P < 0.001).

Conclusion. In the setting of newer stent technology with second-generation everolimus-eluting stents, the risk of death associated with PCI was similar to that associated with CABG for multivessel coronary artery disease. In the long-term, PCI was associated with a higher risk of myocardial infarction and repeat revascularization, whereas CABG was associated with an increased risk of stroke. In the short-term, PCI had lower risks of both death and stroke.

Commentary

Coronary artery disease is a major public health problem. For patients for whom revascularization is deemed to be appropriate, a choice must be made between PCI and CABG. In previous studies that compared PCI and CABG, CABG was shown to have less need for repeat revascularizations as well as mortality benefits [1–3]. However, these prior studies compared CABG with older generations of stents. In the past decade, stent technologies have improved, as the bare-metal stent era gave way to the first generation of of drug-eluting stents (with sirolimus or paclitaxel), to be followed by second-generation drug-eluting stents (with everolimus or zotarolimus) [4].

In this article, Bangalore and colleagues addressed the issue of whether the use of second-generation drug-eluting stents close the outcome gap that favors CABG over PCI in patients with multivessel coronary artery disease. In patients who were considered to have had complete revascularization performed during PCI (ie, revascularization of all major vessels with clinically significant stenosis), they noted mitigation of the outcome differences between the PCI group and the CABG group. They conclude that the decision-making process by patients and their providers regarding revascularization be placed in the context of individual values and preferences.

One major limitation is that the study is an observational study from registry data. Despite the use of sophisticated statistical techniques including propensity score matching to adjust for confounders that are implicit in any nonrandomized comparison of treatment strategies, observational studies suffer from the definitely proof of causality. These limitations are especially important when the two groups being compared have modest differences in outcome.

Applications for Clinical Practice

This observational study, together with a recent randomized clinical trial in which CABG was compared with PCI with the use of everolimus-eluting stents from the BEST trial [5], provided new insights of the 2 revascularization strategies. Clinicians should engage and empower patients with a shared decision-making approach. The early hazard of CABG in stroke and death may be unacceptable to some patients, whereas others might want to avoid the later hazards of PCI in repeat procedure or having a myocardial infarction. Until a definitive study is available, patients should be informed of the best current knowledge of the pros and cons of the two revascularization strategies.

 —Ka Ming Gordon Ngai, MD, MPH

 

Study Overview

Objective. To compare percutaneous coronary intervention (PCI) using second-generation drug-eluting stents (everolimus-eluting stents) with coronary artery bypass grafting (CABG) among patients with multivessel coronary disease.

Design. Observational registry study with propensity-score matching.

Setting and participants. The study relies on patients identified from the Cardiac Surgery Reporting System (CSRS) and Percutaneous Coronary Intervention Reporting System (PCIRS) registries of the New York State Department of Health. These 2 registries were linked to the New York State Vital Statistics Death registry and to the Statewide Planning and Research Cooperative System registry (SPARCS) to obtain further information like dates of admission, surgery, discharge, and death. Subjects were eligible for inclusion if they had multivessel disease (defined as severe stenosis [≥ 70%] in at least 2 diseased major epicardial coronary arteries) and if they had undergone either PCI with implantation of an everolimus-eluting stent or CABG. Subjects were excluded if they had revascularization within 1 year before index procedure; previous cardiac surgery; severe left main coronary artery disease (degree of stenosis ≥ 50%); PCI with a stent other than an everolimus-eluting stent; myocardial infarction without 24 hours before the index procedure; and unstable hemodynamics or cardiogenic shock.

Main outcome measures. The primary outcome of the study was all-cause mortality. Various secondary outcomes included rates of myocardial infarction, stroke, and repeat vascularization.

Main results. Among 116,915 patients assessed for eligibility, 82,096 were excluded. Among 34,819 who met inclusion criteria, 18,446 were included in the propensity score–matched analysis. With a 1:1 matching algorithm, 9223 were in the PCI with everolimus-eluting stent group and 9223 were in the CABG group. Short-term outcomes (in hospital or ≤ 30 days after the index procedure) favored PCI with everolimus-eluting stents over CABG, with a significantly lower risk of death (0.6% vs. 1.1%; hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.35 to 0.69; P < 0.002) as well as stroke (0.2% vs 1.2%; HR, 0.18; 95% CI, 0.11 to 0.29; P < 0.001). The 2 groups had similar rates of myocardial infarction in the short-term (0.5% and 0.4%; HR, 1.37; 95% CI, 0.89 to 2.12; P = 0.16). After a mean follow-up of 2.9 years, there was a similar annual death rate between groups: 3.1% for PCI and 2.9% for CABG (HR, 1.04; 95% CI, 0.93 to 1.17; P = 0.50). PCI with everolimus-eluting stents was associated with a higher risk of a first myocardial infarction than was CABG (1.9% vs 1.1% per year; HR, 1.51; 95% CI, 1.29 to 1.77; P < 0.001). PCI with everolimus-eluting stents was associated with a lower risk of a first stroke than CABG (0.7% vs. 1.0% per year; HR, 0.62; 95% CI, 0.50 to 0.76; P < 0.001). Finally, PCI with everolimus-eluting stents was associated with a higher risk of a first repeat-revascularization procedure than CABG (7.2% vs. 3.1% per year; HR, 2.35; 95% CI, 2.14 to 2.58; P < 0.001).

Conclusion. In the setting of newer stent technology with second-generation everolimus-eluting stents, the risk of death associated with PCI was similar to that associated with CABG for multivessel coronary artery disease. In the long-term, PCI was associated with a higher risk of myocardial infarction and repeat revascularization, whereas CABG was associated with an increased risk of stroke. In the short-term, PCI had lower risks of both death and stroke.

Commentary

Coronary artery disease is a major public health problem. For patients for whom revascularization is deemed to be appropriate, a choice must be made between PCI and CABG. In previous studies that compared PCI and CABG, CABG was shown to have less need for repeat revascularizations as well as mortality benefits [1–3]. However, these prior studies compared CABG with older generations of stents. In the past decade, stent technologies have improved, as the bare-metal stent era gave way to the first generation of of drug-eluting stents (with sirolimus or paclitaxel), to be followed by second-generation drug-eluting stents (with everolimus or zotarolimus) [4].

In this article, Bangalore and colleagues addressed the issue of whether the use of second-generation drug-eluting stents close the outcome gap that favors CABG over PCI in patients with multivessel coronary artery disease. In patients who were considered to have had complete revascularization performed during PCI (ie, revascularization of all major vessels with clinically significant stenosis), they noted mitigation of the outcome differences between the PCI group and the CABG group. They conclude that the decision-making process by patients and their providers regarding revascularization be placed in the context of individual values and preferences.

One major limitation is that the study is an observational study from registry data. Despite the use of sophisticated statistical techniques including propensity score matching to adjust for confounders that are implicit in any nonrandomized comparison of treatment strategies, observational studies suffer from the definitely proof of causality. These limitations are especially important when the two groups being compared have modest differences in outcome.

Applications for Clinical Practice

This observational study, together with a recent randomized clinical trial in which CABG was compared with PCI with the use of everolimus-eluting stents from the BEST trial [5], provided new insights of the 2 revascularization strategies. Clinicians should engage and empower patients with a shared decision-making approach. The early hazard of CABG in stroke and death may be unacceptable to some patients, whereas others might want to avoid the later hazards of PCI in repeat procedure or having a myocardial infarction. Until a definitive study is available, patients should be informed of the best current knowledge of the pros and cons of the two revascularization strategies.

 —Ka Ming Gordon Ngai, MD, MPH

 

References

1. Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet 2013;381: 639–50.

2. Hannan EL, Racz MJ, Arani DT, et al. A comparison of short- and long-term outcomes for balloon angioplasty and coronary stent placement. J Am Coll Cardiol 2000;36:395–403.

3. Hannan EL, Racz MJ, Walford G, et al. Long-term outcomes of coronary-artery bypass grafting versus stent implantation. N Engl J Med 2005;352: 2174–83.

4. Harrington RA. Selecting revascularization strategies in patients with coronary disease. N Engl J Med 2015;372: 1261–3.

5. Park SJ, Ahn JM, Kim YH, et al. Trial of everolimus-eluting stents or bypass surgery for coronary disease. N Engl J Med 2015;372:1204–12.

References

1. Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet 2013;381: 639–50.

2. Hannan EL, Racz MJ, Arani DT, et al. A comparison of short- and long-term outcomes for balloon angioplasty and coronary stent placement. J Am Coll Cardiol 2000;36:395–403.

3. Hannan EL, Racz MJ, Walford G, et al. Long-term outcomes of coronary-artery bypass grafting versus stent implantation. N Engl J Med 2005;352: 2174–83.

4. Harrington RA. Selecting revascularization strategies in patients with coronary disease. N Engl J Med 2015;372: 1261–3.

5. Park SJ, Ahn JM, Kim YH, et al. Trial of everolimus-eluting stents or bypass surgery for coronary disease. N Engl J Med 2015;372:1204–12.

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Journal of Clinical Outcomes Management - May 2015, VOL. 22, NO. 5
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Decision Making in Venous Thromboembolism

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Decision Making in Venous Thromboembolism

From the Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA.

 

Abstract

  • Objective: To review the diagnosis and management of venous thromboembolism (VTE).
  • Methods: Review of the literature.
  • Results: VTE and its associated complications account for significant morbidity and mortality. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. Clinical decision rules have been developed  that can help determine which patients warrant further testing. Anticoagulation, the mainstay of VTE treatment, increases bleeding risk, necessitating tailored treatment strategies that must incorporate etiology, risk, benefit, cost, and patient preference.
  • Conclusion: Further study is needed to understand individual patient risks and to identify treatments that will lead to improved patient outcomes.

 

Venous thromboembolism (VTE) and its associated complications account for significant morbidity and mortality. Each year between 100 and 180 persons per 100,000 in Western countries develop VTE. The majority of VTEs are classified as either pulmonary embolism (PE), which accounts for one third of the events, or deep vein thrombosis (DVT), which is responsible for the remaining two thirds. Between 20% and 30% of those patients diagnosed with thrombotic events will die within the first month after diagnosis [1].PE is a common consequence of DVT; 40% of patients who are diagnosed with DVT will be subsequently found to have PE upon further imaging. This high rate of association is also seen in those who present with PE, 70% of whom will also be found to have concomitant DVT [2,3].

There are many risk factors for VTE, including patient-specific demographic factors, environmental factors, and pharmacologic factors (Table 1). One of the main demographic factors associated with development of VTE is age. It is rare for children to suffer a thrombotic event, whereas older persons have a risk of 450 to 600 events per 100,000 [1]. Other demographic risk factors, both inherited and acquired, have been associated with increased risk of VTE. Inherited risk factors include factor V leiden mutation, prothrombin gene mutation, protein C and protein S deficiencies, antithrombin deficiency, and dysfibrinogenemia. The prevalence of these inherited thrombophilias in patients with VTE is about 25% to 35% compared to 10% in controls without VTE [4,5].Acquired risk factors include prior VTE, malignancy, surgery, trauma, obesity, smoking, pregnancy, and immobilization [6–9].Additionally, multiple medical conditions, including the antiphospholipid antibody syndrome, myeloproliferative neoplasms, paroxysmal nocturnal hemoglobinuria, renal disease (particularly nephritic syndrome), liver disease, and inflammatory bowel disease have been shown to increase risk of VTE [10–13].

Anatomic risk factors include Paget-Schroetter syndrome (compression of upper extremity veins due to abnormalities at the thoracic outlet), May-Thurner syndrome (significant compression of the left common iliac vein by the right common iliac artery), and abnormalities of the inferior vena cava [14–16].Medications that are associated with increased risk of VTE include but are not limited to estrogen (both in oral contraceptives as well as hormone replacement therapy) [17,18],the selective estrogen receptor modulator tamoxifen [19],testosterone [20],and glucocorticoids [21].It is important to note that many patients with VTE have more than one acquired risk factor for thrombosis [22],and also that acquired risk factors are more likely to lead to VTE in the setting of underlying inherited thrombophilic conditions [23].

Pathogenesis

Abnormalities in both coagulation factors and the vascular bed are at the core of the pathogenesis of VTE. The multifaceted etiology of thrombosis was first described in 1856 by Virchow, who defined a triad of defects in the vessel wall, platelets, and coagulation proteins [24].Usually the vessel wall is lined with endothelial cells that provide a nonthrombotic surface and limit platelet aggregation through release of prostacyclins and nitric oxide. When the endothelial lining is compromised, the homeostatic surveillance system is disturbed and platelet activation and the coagulation system are initiated. Tissue factor exposure in the damaged area of the vessel leads to activation of the coagulation cascade. Collagen that is present in the area of the wound is also exposed and can activate platelets, which provide the phospholipid surface upon which the coagulation cascade occurs. Platelets initially tether to the exposed collagen through binding of glycoprotein Ib-V-IX in association with von Willebrand factor [25].The thrombus is initiated as more platelets are recruited to exposed collagen of the injured endothelium through aggregation in response to the binding of GPIIIb/IIa with fibrinogen. This process is self-perpetuating as these activated platelets release additional proteins such as adenosine diphosphate (ADP), serotonin, and thromboxane A2, all of which fuel the recruitment and activation of additional platelets [26].

Diagnosis

The key to decreasing the morbidity and mortality associated with VTE is timely diagnosis and early initiation of therapy. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. DVT is usually associated with pain in calf or thigh, unilateral swelling, tenderness, and redness. PE can present as chest pain, shortness of breath, syncope, hemoptysis, and/or cardiac palpitations.

Decision Rules

Clinical decision rules based on signs, symptoms, and risk factors have been developed to estimate the pretest probability of PE or DVT and to help determine which patients warrant further testing. These clinical decision rules include the Wells criteria (separate rules for DVT and PE) [27,28],as well as the Geneva score [29],which is focused on identifying patients with a likelihood of having a PE. In general, these clinical rules are applied at presentation to predict the risk of VTE, and patients who score high are evaluated by imaging modalities, while those with lower scores should be considered for further stratification based on D-dimer testing. The goal of clinical assessment and use of a decision rule is to identify patients at low risk of VTE to reduce the number of imaging studies performed. Most of the decision rules focus on the use of noninvasive evaluations that are easily implemented, including clinical history and presentation, abnormalities in oxygen saturation, chest radiography findings, and electro-cardiography.

D-Dimer Testing

D-dimer testing is at the core of all predictive models for VTE. D-dimer is a fibrin degradation product that is detectable in the blood during active fibrinolysis and occurs after clot formation. The concentration of D-dimer increases in patients with active clot. D-dimer testing is usually performed as a quantitative ELISA or automated turbidometric assay and is highly sensitive (> 95%) in excluding a diagnosis of VTE if results are in the normal range [30].The presence of a normal D-dimer and a low probability based on clinical assessment criteria can be integrated to determine which patients have a low (generally < 99%) likelihood of having VTE [31].It should be noted that other factors can lead to an increased D-dimer, including malignancy, trauma, critical illness, disseminated intravascular coagulation, pregnancy, infection, and postoperative status, which can produce false-positive results and cloud the utility of the test in excluding those at low risk of VTE from undergoing imaging [32–34].Additionally, D-dimer values naturally increase with age and recent work has shown utility of an age-adjusted D-dimer threshold, though this method is not yet widespread in clinical practice [35,36].

Imaging

After application of a clinical prediction rule, the mainstay of diagnosis of VTE is imaging. For DVT the use of ultrasonography is considered the gold standard, with both high sensitivity (89–100%) and specificity (86–100%), especially when the DVT is located proximally [37–39].We generally recommend compression ultrasound starting with the proximal veins but expanding to include the whole leg if the proximal studies are negative [40–42].Other diagnostic options include computed tomography (CT) venography, which is not first line as it is highly invasive and exposes the patient to iodine-based contrast dyes, and magnetic resonance venography (MRV), which offers superb visualization for diagnosis of pelvic vein thrombosis but is limited because of availability and cost issues.

Helical CT pulmonary angiography (CTPA) is the diagnostic test of choice in PE, with high sensitivity (96%) and specificity (95%), and has replaced conventional ventilation perfusion (VQ) scanning or other methods such as magnetic resonance pulmonary angiography in most settings [43,44].CTPA should be avoided in patients who have severe chronic kidney disease or a contrast allergy, and is often avoided in patients who are pregnant due to potential risk of radiation exposure, and in such situations VQ scanning may be employed.

Algorithmic Approach to Workup

Our general practice is to apply the Wells clinical prediction rule (Table 2 for DVT and Table 3 for PE), as this system is likely the most familiar to a large number of clinicians and a score can be obtained promptly but accurately based on easily accessible data from history and exam. We generally use the simplified modified criteria presented in the Tables. Once the clinical prediction rule has been applied, we use 2 risk-based algorithms for further evaluation (Figure 1 and Figure 2) [45,46]. In general, we initially perform a D-dimer test for low-risk patients, while we advocate for prompt imaging in high-risk patients to avoid delays in treatment should VTE be diagnosed. Once a diagnosis of VTE is established, treatment should be started promptly. One exception may be isolated 
distal DVT, where it is reasonable to defer treatment in favor of serial ultrasound testing to rule to rule out proximal extension unless the patient is significantly symptomatic with the distal DVT alone [40].

Of note, there are multiple clinical situations in which the application of a clinical prediction rule followed by D-dimer testing and/or imaging cannot be “standardized” with such algorithms. These include situations where D-dimer may be falsely positive (as above), situations in which alternative imaging strategies should be used to avoid contrast exposure in workup of PE (as above), and workup of suspected upper extremity DVT. Upper extremity ultrasound comprises about 10% of all DVT and frequently occurs in the setting of risk factors such as central venous catheters or pacemakers; specific upper-extremity risk-assessment rules have been developed [47,48].

 D-dimer is generally not as useful in workup of upper extremity DVT (given high prevalence of factors that lead to false-positive DVT) and we generally perform compression ultrasonography up front in patients in whom we have high clinical suspicion for upper extremity DVT. In all such clinical situations above, workup should be individualized in accordance with patient factors and careful physician assessment.

Acute Treatment Options

The first step in treatment is identification of patients who are at high risk of 

VTE-related mortality, especially those with PE and hemodynamic instability (defined as systolic blood pressure < 90 mm Hg or a drop in pressure more than 40 mm Hg for more than 15 minutes in the absence of new-onset arrhythmia, hypovolemia, and sepsis). This patient population should be considered for emergent management with thrombolytic therapy, typically recombinant tissue plasminogen activator (t-PA, alteplase). Thrombolysis should be reserved for those who have not had any surgical procedures in the last 2 weeks, have no evidence of neurosurgical bleeding, and are not at risk of a bleeding diathesis. Patients who present without frank hemodynamic instability but have evidence of right ventricular dysfunction (by echocardiography or biomarkers such as troponin elevation) may be at “intermediate risk” for adverse outcomes and the role of thrombolytics in this population is an area of active investigation [49,50].

In standard cases of DVT and PE without hemodynamic compromise, the current standard of care is to initiate parenteral anticoagulation. The immediate goal of therapy is to treat rapidly with anticoagulants to prevent the thrombus from propagating further and to prevent DVT from embolization to the lungs or other vascular beds. The initial treatment of VTE has been extensively discussed and guidelines have been established with recommendations for initiation of anticoagulation; the American College of Chest Physicians (ACCP) released the 9th edition of their guidelines in 2012 based on consensus agreements derived from primary data [51].

Heparin-based drugs are the mainstay of initial treatment. These drugs act by potentiating antithrombin and therefore inactivating thrombin and other coagulation factors such as Xa. Unfractionated heparin (UFH) can be administered as an initial bolus followed by a continuous infusion with dosing being based on weight and titrated to activated partial thromboplastin time (aPTT) or the anti-factor Xa level. Alternatively, patients may be treated with a low molecular weight heparin (LMWH) administered subcutaneously in fixed weight-adjusted doses, which obviates the need for monitoring in most cases [52].LMWHs work in a similar manner to UFH but have more anti-Xa activity in comparison to anti-thrombin activity. LMWH appears to be more effective than UFH for initial treatment of VTE and has been associated with lower risk of major hemorrhage [53].The options for treatment of VTE have expanded in recent years with the approval of fondparinux, a pentasaccharide specifically targeted to inhibit factor Xa. Fondaparinux has been shown to have similar efficacy to LMWH in patients with DVT [54],and while it has not been evaluated directly against LMWH for initial treatment of PE it has been shown to be at least as effective and safe as UFH [55].

Both LMWH and fondaparinux are cleared renally and therefore have increased bleeding risk in patients with renal impairment. In patients with creatinine clearance of less than 30 mL/min, dose reduction or lengthening of dosing interval may be appropriate. Anti-factor Xa activity can be used as a functional assay to monitor and titrate the level of anticoagulation in patients treated with UFH, LMWH, and fondaparinux. Monitoring is useful in the setting of impaired renal function (as above) in addition to extremes of body weight and pregnancy. When used for monitoring of UFH, the anti-factor Xa activity can be measured at any time during administration with a therapeutic goal range of 0.3–0.7 international units (IU)/mL. When used for LMWH, a “peak” anti-factor Xa should be measured approximately 4 hours after dosing, with therapeutic goals depending on preparation and schedule of treatment but generally between 0.6 to 1.0 IU/mL for twice daily and around 1.0 -2.0 IU/mL for once-daily [56].For patients on dialysis, we generally use intravenous UFH for acute treatment of VTE, though recent work has shown that enoxaparin (doses of 0.4 to 1 mg/kg/day) was as safe as UFH with respect to bleeding and was associated with shorter hospital length of stay [57].For long-term treatment of VTE, warfarin is generally preferred based on clinical experience with this agent, though small studies have suggested that parenteral agents may be useful alternatives to warfarin [58].

In many patients who are clinically stable without significant medical comorbidities, outpatient administration of these medications without hospitalization is considered safe. Patients with DVT are often safe to manage as outpatients unless significant clot burden is present and thrombolysis is being considered. For PE, the pulmonary embolism severity index (PESI) and simplified index (sPESI) may be useful to risk-stratify patients and identify those at low risk of complications who may be suitable for outpatient treatment [59,60].Studies have shown that hemodynamically stable patients who did not require supplemental oxygenation or have contraindications to LMWH therapy were safely managed as outpatients with low risk of recurrent VTE and bleeding [61,62].One exception may be patients with intermediate risk PE, who are hemodynamically stable but have evidence of right ventricular dysfunction and may be better served by an initial in-hospital observation period, especially if thrombolysis is being considered.

Most patients who present with VTE are transitioned to warfarin for long-term therapy. Warfarin can be started on the same day as parenteral anticoagulation. Both drugs are overlapped for at least 5 days, with a target INR of 2.0–3.0. Patients may achieve the target INR level quickly because factor VII has a short half-life and the level drops quickly; however, the overlap of 5 days is essential even when the INR is in the target range because a full anticoagulant affect is not achieved until prothrombin levels decline, and this is a slow process due to the long half-life of prothrombin. Warfarin also causes rapid decrease in levels of natural anticoagulants such as protein C and protein S, which further exacerbates the net hypercoagulable state in the short-term. Warfarin without a bridging parenteral agent carries a risk of warfarin-induced skin necrosis [63]and is not effective as an initial anticoagulant treatment in acute VTE as there is a relatively high risk of symptomatic clot extension or recurrent VTE compared to warfarin with use of a bridging agent [64].In specific cases such as cancer-associated VTE (see discussion below), LMWH is preferred to warfarin for long-term active therapy.

Long-Term Active Therapy After Acute Treatment

Duration of Anticoagulation

Recommended duration of anticoagulation depends on a myriad of factors including severity of VTE, risk of recurrence, bleeding risk, and lifestyle modification issues, as well as on the safety and availability of alternative therapies such as low-intensity warfarin, aspirin, or the new oral anticoagulants. The decision tree for length of treatment starts with whether the VTE was a provoked or a spontaneous event. Provoked events occur when the event is associated with an identifiable risk factor, such as immobilization from prolonged medical illness or surgical intervention, pregnancy or oral contraceptive use, and prolonged air travel.

Consensus guidelines suggest that 3 months of anti-coagulation are generally sufficient treatment for a provoked VTE [51,65,66]. Data from multiple studies and a meta-analysis suggests that less than 3 months of anticoagulation (4 to 6 weeks in most trials) is associated with an approximately 1.5-fold higher risk of recurrent VTE than 3 months [67,68].However, data from this meta-analysis also suggests that anticoagulation for longer than 3 months (6 to 12 months in most trials) is not associated with higher rates of recurrent VTE. We generally anticoagulate for 3 months in patients with provoked VTE.

Determining the duration of anticoagulation is more complex in patients with idiopathic/unprovoked VTE. Kearon and colleagues found that in patients with first idiopathic VTE, patients who were anticoagulated for 24 months versus 3 months had lower risk of recurrent VTE (1.3% per patient-year with 24 months versus 27.4% per patient-year with 3 months) [69].Similar studies and meta-analyses have demonstrated decreased recurrence rates in patients anticoagulated for a prolonged period of time. However, one study of prolonged anticoagulation revealed that at 3 years there was no difference in recurrence rate in patients with PE who were anticoagulated for 6 months versus 1 year [70].The likelihood of recurrent DVT in patients with first episode of idiopathic proximal DVT treated with either 3 months or 12 months of warfarin was similar after treatment was discontinued [71].Prolonged periods of anticoagulation do not directly influence risk of recurrence but instead may only delay occurrence of a second event [72].For that reason, the decision is essentially whether to anticoagulate for 3 months or to continue therapy indefinitely [73]. Current guidelines recommend continuing anticoagulation for 3 months in those at high risk of bleeding, and continuing for an extended duration in those at low or moderate bleeding risk [51]. Patients' values and perferences should be entertained and decisions made on a patient-by-patient basis.

For patients at high risk of recurrent VTE, we generally recommend indefinite anticoagulation unless the patient has a significantly elevated bleeding risk or strongly prefers to discontinue anticoagulation and compliance concerns are evident. High-risk patients are those who have suffered from multiple episodes of recurrent VTE, those who have clotted while being anticoagulated, and those with acquired risk factors, such as antiphospholipid antibodies and malignancy. Other high-risk groups are those with high-risk thrombophilias such as deficiency of protein S, protein C, or antithrombin, homozygous factor V Leiden or prothrombin gene mutations, and compound heterozygous factor V Leiden/prothrombin gene mutation in the setting of an unprovoked event. Further discussion of models for risk assessment of recurrence is provided below.

Assessment of Bleeding Risk

The bleeding risk associated with the use of anticoagulation must be weighed against the risk of clotting events when determining duration of anticoagulation, especially in those patients for whom indefinite anticoagulation is a consideration. Risk of bleeding while on anticoagulation is approximately 1–3% per 100 patient-years [74],but concomitant medical conditions such as renal failure, diabetes-related cerebrovascular disease, malignancy, advanced age, and use of antiplatelet agents all increase the risk of bleeding. Bleeding risk is highest when patients first initiate anticoagulation and is approximately 10 times the risk in the first month of therapy than after the first year of therapy [75].

Risk assessment models such as the RIETE score may be helpful when indefinite anticoagulation is a possibility [76].The RIETE score encompasses 6 risk factors (age > 75 years, recent bleeding, cancer, creatinine level > 1.2 mg/dL, anemia, or PE at baseline) to categorize patients into low risk (0 points, 0.3% risk of bleeding), intermediate risk (1–4 points, 2.6% risk of bleeding) and high risk (> 4 points, 6.2% risk of bleeding) within 3 months of anticoagulant therapy. The ACCP has developed a more extensive list of 17 potential risk factors for bleeding to categorize patients into low risk (no risk factors, 0.8%/year risk of bleeding), intermediate risk (1 risk factor, 1.6%/year risk of bleeding) and high risk (2 or more risk factors, >6.5%/year risk of bleeding) categories [77].The RIETE score is simpler to use but was not developed for assessing risk of bleeding during indefinite therapy, while the ACCP risk categorization predicts a yearly risk and is therefore applicable for long-term risk assessment but is more cumbersome to use. In practice, we generally use a clinical gestalt of a patient’s clinical risk factors (particularly age, renal or hepatic dysfunction, and frequent falls) to assess if they may be at high risk of bleeding and if the risk of indefinite anticoagulation may thus outweigh the potential benefit.

We also note that several scoring systems (HAS-BLED, HEMORR2HAGES, and ATRIA scores) have been developed to predict those at high risk of bleeding on anticoagulation for atrial fibrillation [78–80].These scores generally include similar clinical risk factors to those in the RIETE and ACCP scoring systems. Several studies have compared the HAS-BLED, HEMORR2HAGES, and ATRIA scores and a systematic review and meta-analysis concluded that the HAS-BLED score is recommended, due to increased sensitivity and ease of application [81].However, as these scores have not been validated for anticoagulation in the setting of VTE, we do not use them in this capacity.

Risk Stratification for Recurrent VTE

When predicting risk of recurrent VTE, clinical risk factors including obesity, male gender, and underlying thrombophilia (including the “high risk” inherited thrombophilias identified above) must taken into consideration. Location of the thrombus must also be considered; it has also been demonstrated that patients with DVT involving the iliofemoral veins are at higher risk of recurrence than those without iliac involvement [82].Other factors that may be useful in risk stratification include D-dimer level and ultrasound to search for residual venous thrombosis.

D-dimer Levels

D-dimer levels are one of the more promising methods for assessing the risk of recurrent VTE after cessation of anticoagulation, especially in the case of idiopathic VTE where indefinite anticoagulation should be considered but may pose either risk of bleeding or significant inconvenience to patients. A normal D-dimer measured 1 month after cessation of anticoagulation offers a high negative predictive value for risk of recurrence [83].A number of studies have demonstrated that patients with elevated D-dimer 1 month after anticoagulation cessation are at increased risk for a recurrent event [84–86].Two predictive models that have been developed incorporate D-dimer testing into decision making [87,88].The DASH predictive model relies on the D-dimer result in addition to age, male sex, and use of hormone therapy as a method of risk stratification for recurrent VTE in patients with a first unprovoked event. Using this scoring system, patients with a score of 0 or 1 had a recurrence rate of 3.1%, those with a score of 2 a recurrence rate of 6.4%, and those with a score of 3 or greater a recurrence rate of 12.3%. The authors postulate that by using this assessment scheme they can avoid lifelong anticoagulation in 51% of patients. The Vienna prediction model uses male sex, location of VTE (proximal DVT and PE are at higher risk), and D-dimer level to predict risk of recurrent VTE. This model has recently been updated to include a “dynamic” component to predict risk of recurrence of VTE from multiple random time points [89].

Overall, D-dimer may be useful for risk stratification. We often employ the method of stopping anticoagulation in patients with unprovoked VTE after 3 months (if the patient has no identifiable clinical risk factors that place them at high risk of recurrence) and testing D-dimer 1 month after cessation of anticoagulation. An elevated D-dimer is a solid reason to restart anticoagulation (potentially on an indefinite basis), while a negative D-dimer provides support for withholding further anticoagulation in the absence of other significant risk factors for recurrence. However, lack of agreement regarding assay cut-points as well as multiple reasons other than VTE for D-dimer elevation may limit widespread use of this method. We generally use a cutpoint of 250 ug/L as “negative,” though at least one study showed that cut-points of 250 ug/L versus 500 ug/L did not change the utility of this method [90].In our practice, risk prediction models are most useful to provide patients with additional information and a visual presentation to support our recommendation. This is particularly true of the Vienna prediction rule, which is available in a printable nomogram which can be distributed to patients and completed together during the clinic visit.

Imaging Analysis

Imaging analysis may also assist with risk stratification. Clinical assessment modules have been developed that incorporate repeat imaging studies for assessment of recannulization of affected veins. In patients with residual vein thrombosis (RVT) at the time anticoagulation was stopped, the hazard ratio for recurrence was 2.4 compared to those without RVT [91].There are a number of ways RVT could impact recurrence, including inpaired venous flow leading to stasis and activation of the coagulation cascade. Subsequent studies used serial ultrasound to determine when to stop anticoagulation. In one study, patients were anticoagulated for 3 months and for those that had RVT, anticoagulation was continued for up to 9 months for provoked and 21 months for unprovoked VTE. In comparison to fixed dosing of 6 months of anti-coagulation, those who had their length of anticoagulation tailored to ultrasonography findings had a lower rate of recurrent VTE [92].Limitations to using RVT in clinical decision-making include lack of a standard definition of RVT and variability in both timing of ultrasound (operator variability) and interpretation of results [93].

Other Options

Another option in patients who are being considered for indefinite anticoagulation is to decrease the intensity of anticoagulation. Since this would theoretically lower the risk of bleeding, the perceived benefit of long-term, low-intensity anticoagulation would be reduction in both bleeding and clotting risk. The PREVENT trial randomized patients who had received full-dose anticoagulation for a median of 6.5 months to either low-intensity warfarin (INR goal of 1.5-2.0 instead of 2.0-3.0) or placebo. In the anticoagulation group, there was a 64% risk reduction in recurrent VTE (hazard ratio 0.36, 95% CI 0.19 to 0.67) but an increased risk of bleeding (hazard ratio 1.92, 95% CI 1.26 to 2.93) [94].The ELATE study randomized patients with unprovoked VTE who had completed 3 or more months of full-intensity warfarin therapy (target INR 2.0–3.0) to continue therapy with either low-intensity warfarin (target INR 1.5–2.0) or full-intensity warfarin (target INR 2.0-3.0). Compared to the low-intensity group, the conventional-intensity group had lower rates of recurrent VTE and no increased rates of major bleeding [95].This study, however, has been criticized because of its overall low bleeding rate in both treatment groups.

Aspirin is an option in patients in whom long-term anticoagulation is untenable. The ASPIRE trial demonstrated that in patients with unprovoked VTE who had completed a course of initial anticoagulation, aspirin 100 mg daily reduced the risk of major vascular events compared to placebo with no increase in bleeding [96].However, aspirin was not associated with a significant reduction in risk of VTE alone (only the composite vascular event endpoint). The WARFASA trial, however, demonstrated that aspirin 100 mg daily was associated with a significant reduction in recurrent VTE compared to placebo after 6 to 18 months of anticoagulation without an increase in major bleeding [97].The absolute risk of recurrence was 11% in the placebo group and 5.9% in the aspirin group. More recently, the INSPIRE collaboration analyzed data from both trials and found that aspirin after initial anticoagulation reduced the risk of recurrent VTE by approximately 42% with a low rate of major bleeding [98].The absolute risk reduction was even larger in men and older patients. For this reason, we recommend aspirin to those patients in whom indefinite anticoagulation may be warranted from the standpoint of reducing risk of recurrent VTE but in whom the risk of bleeding precludes its use.

Hypercoagulable States In Specific Populations

Inherited Thrombophilias

Patients with a hereditary thrombophilia are at increased risk for incident VTE [99].These inherited mutations result in either a loss of normal anticoagulant function or gain of a prothrombotic state. Hereditary disorders associated with VTE include deficiency of antithrombin, protein C, or protein S, or the presence of factor V Leiden and/or prothrombin G20210A mutations. Although deficiency of protein C, protein S, or antithrombin is uncommon and affects only 0.5% of the population, these states have been associated with a 10-fold increased risk of thrombosis in comparison to the general population. Factor V Leiden and prothrombin gene mutation are less likely to be associated with incident thrombosis (2 to 5-fold increased risk of VTE) and are more prevalent in the Caucasian population [100].Though these hereditary thrombophilias increase risk of VTE, prophylactic anti-coagulation prior to a first VTE is not generally indicated.

Data regarding the impact of the inherited thrombophilias on risk of recurrent VTE is less well defined. While some data suggest that inherited thrombophilias are associated with increased risk of recurrent VTE, the degree of impact may be clinically modest especially in those with heterozygous factor V Leiden or prothrombin gene mutations [101].Ideally, a clinical trial would be designed to assess whether hereditary thrombophilia testing is beneficial for patients with VTE in decision-making regarding length of anticoagulation, type of anticoagulation, and risk of recurrence. If a patient with a low-risk inherited thrombophilia has a DVT in the setting of an additional provoking risk factor (surgery, pregnancy, etc), a 3-month course of anticoagulation followed by D-dimer assessment as above is reasonable. If a patient with an inherited thrombophilia experiences an idiopathic VTE, or if a patient with a “high-risk” thrombophilia as described above experiences any type of VTE, we generally recommend indefinite anticoagulation in the absence of high bleeding risk, though again this is a very patient-dependent choice.

Acquired Thrombophilias

Antiphospholipid Syndrome

Antibodies directed against proteins that bind phospho-lipids are associated with an acquired hypercoagulable state. The autoantibodies are categorized as antiphospho-lipid antibodies (APLAs), which include anticardiolipin antibodies (IgG and IgM), beta-2 glycoprotein 1 antibodies (anti-B2 GP), and lupus anticoagulant. These antibodies can form autonomously, as seen in primary disorders, or in association with autoimmune disease as a secondary disorder.

Criteria have been developed to distinguish antiphospholipid-associated clotting disorders from other forms of thrombophilia. The updated Sapporo criteria depend on both laboratory and clinical diagnostic criteria [102].The laboratory diagnosis of APLAs requires the presence of lupus anticoagulants, anticardiolipin antibodies, or anti-B2 GP on at least 2 assays at least 12 weeks apart with elevation above the 99th percentile of the testing laboratory’s normal distribution [103].Testing for lupus anticoagulant is based on 3 stages, the first of which is inhibition of phospholipid-dependent coagulation tests with prolonged clotting time (eg, aPTT or dilute Russell’s viper venom time). The diagnosis is confirmed by a secondary test in which excess hexagonal phase phospholipids are added to incubate with the patient’s plasma to absorb the APLA [104].The presence of anticardiolipin antibodies and anti beta-2 GP antibodies is determined using ELISA based immunoassays. Unlike most other thrombophilias, antiphospholipid syndrome is associated with both arterial and venous thromboembolic events and may be an indication for lifelong anticoagulation after a first thrombotic event. We generally recommend indefinite anticoagulation in the absence of significant bleeding risk.

Cancer-Associated Hypercoagulable State

Patients with cancer have a propensity for thromboembolic events. The underlying mechanisms responsible for cancer-associated clotting events are multifactorial and an area of intense research. Tumor cells can initiate activation of the clotting cascade through release of tissue factor and other pro-coagulant molecules [105].Type and stage of cancer impact risk of VTE, and the tumor itself can compress vasculature leading to venous stasis. Furthermore, chemotherapy, hormone therapy, antiangiogenic drugs, erythropoietin agents, and indwelling central venous catheters all are associated with increased risk of thrombotic events. Approximately 25% of all cancer patients will experience a thrombotic event during the course of their disease [106]. In fact, the presence of a spontaneous clot may be a harbinger of underlying malignancy [107].Approximately 10% of patients who present with an idiopathic VTE are diagnosed with cancer in the next 1 to 2 years.

The utility of extensive cancer screening in patients with spontaneous clotting events is often debated. The small studies that have addressed cancer associated clots have not demonstrated any mortality benefit with extensive screening. A prospective cohort study addressed the utility of limited versus extensive screening [108].In this study, all patients underwent a series of basic screening tests such as history taking, physical examination, chest radiograph, and basic laboratory parameters. Approximately half of the patients underwent additional testing (CT of chest and abdomen and mammography for women). Screening did not result in increased survival or fewer cancer-related deaths. 3.5 % of patients in the extensive screening group were diagnosed with malignancy in comparison to 2.4% in the limited screening group. During follow-up, cancer was diagnosed in 3.7% and 5.0% in the extensive and limited screening groups, respectively. The authors concluded that the low yield of extensive screening and lack of survival benefit did not warrant routinely ordering cancer screening tests above and beyond age-appropriate screening in patients with idiopathic VTE. However, it is known that identification of occult malignancy at an earlier stage of disease is beneficial, and cancer diagnosed within one year of an episode of VTE is generally more advanced and associated with a poorer prognosis [109].It is our practice to take a through history from patients with unprovoked clots particularly focusing on symptoms suggestive of an underlying cancer. We recommend that patients be up to date with all age-appropriate cancer screening.

Heparin-based products (rather than warfarin) are recommended for long-term treatment of cancer-associated DVT. Several trials, most prominently the CLOT trial, have demonstrated that LMWH is associated with reduced risk of recurrent VTE compared with warfarin in cancer patients [110].Fondaparinux may be a reasonable alternative if a patient is unable to tolerate a LMWH. In terms of treatment duration, patients with cancer-associated VTE should be anticoagulated indefinitely as long as they continue to have evidence of active malignancy and/or remain on antineoplastic treatment [111].

Heparin has potential anticancer effects beyond its anticoagulation properties. It is believed that heparin use in patients with cancer can influence cancer progression by acting as an antimetastatic agent. The molecular mechanisms underlying this significant observation are not completely understood, although the first documented benefit of these drugs dates back to the 1970s [112].Overall, LMWH have been associated with improved overall survival in cancer patients and this effect appears to be distinct from its ability to prevent life-threatening VTE episodes [113].

Estrogen-Related Thromboembolic Disease

Pregnancy is a well-established acquired hypercoagulable state, and thromboembolic disease accounts for significant morbidity and mortality in pregnancy and the postpartum period. Approximately 1 in 1000 women will suffer from a thrombotic event during pregnancy or shortly after delivery [8]. The etiology of the tendency to clot during pregnancy is multifactorial and mainly reflects venous stasis due to vasculature compression by the uterus, changes in coagulation factors as the pregnancy progresses, and endothelial damage during delivery, especially Cesarean section. Both factor VIII and von Willebrand factor levels increase, especially in the final months of pregnancy. Simultaneously, levels of the natural anticoagulant protein S diminish, leading to an acquired resistance to activated protein C which results in increased thrombin generation and therefore a hypercoagulable state [114].The risk of thrombosis in pregnancy is clearly heightened in women with inherited thrombophilias, especially in the postpartum period [115].

Similarly to pregnancy, hormone-based contraceptive agents and estrogen replacement therapies are also associated with increased thrombotic risk. Over the years, drug manufacturers have tried to mitigate the clotting risk associated with these drugs by reducing the amount of estrogen and altering the type of progesterone used, yet a risk still remains, resulting in a VTE incidence 2 to 7 times higher in this population [116].The risk is highest in the first 4 months of use and is unaffected by duration of use; risk extends for 3 months after cessation of estrogen-containing therapy. Patients who develop VTE while taking an oral contraceptive are generally instructed to stop the contraceptive and consider an alternative form of birth control. Although routine screening for thrombophilia is not offered to women before prescribing oral contraceptives, a thorough personal and family history regarding venous and arterial thrombotic events as well as recurrent pregnancy loss in women should be taken to evaluate thromboembolic risk factors. We generally avoid use of oral contraceptives in patients with a known hereditary thrombophilia, and consider screening prior to initiation of therapy in those with a strong family history of VTE.

Superficial VTE

Although the main disorders that comprise VTE are DVT and PE, another common presentation is superficial venous thromboembolism (SVT). The risk factors for developing an SVT are similar to those for DVT. In addition, varicose veins also increase the incidence of developing SVT [117].SVT is not associated with excessive mortality, and the main concern with it is progression to DVT. About 25% of patients diagnosed with SVT may have DVT or PE at the time of diagnosis and about 3% without DVT or PE at time of diagnosis developed one of these complications over the following 3 months; clot propagation is another common complication [118].Ultrasound may be of utility in diagnosing occult DVT in patients who initially diagnosed with SVT [119].

For patients who have only SVT at baseline without concomitant DVT or PE, it is difficult to determine which patients are at risk for developing DVT. Some risk stratification models include clot location. Since SVT clots usually develop in the saphenous vein, the clot would need to either progress from the sapheno-femoral junction to the common femoral vein; thus, any clots located near the sapheno-femoral junction are at risk of progressing into the deep vasculature [120].Clots within 3 cm of the junction may be more likely to progress to DVT [121].Chengelis and colleagues feel that proximal saphenous vein thrombosis should likely be treated with anticoagulation [122].Others have taken a more general approach, stating that all clots above the knee or in the thigh area should be treated aggressively [123].

There are solid data for the use of anticoagulation in SVT. In the STEFLUX (Superficial ThromboEmbolism and Fluxum) study, participants received the LMWH parnaparin at one of 3 doses: 8500 IU once daily for 10 days followed by placebo for 20 days, 8500 IU once daily for 10 days and then 6400 IU once daily for 20 days, or 4250 IU once daily for 30 days. Those who received the intermediate dosing had lower rates of DVT, PE, and relapse/SVT recurrence in the first 33 days [124].In the CALISTO trial, fondaparinux 2.5mg per day for 45 days effectively reduced the risk of symptomatic DVT, PE, or SVT recurrence or extension and was not associated with any increased major bleeding compared to placebo [125].A Cochrane review included 30 studies involving over 6500 participants with SVT of the lower extremities. The treatments used in these studies included fondaparinux, LMWH, UFH, non-steriodal anti-inflammatory agents, topical treatment, and surgery. According to the findings, use of fondaparinux at prophylactic dosing for 6 weeks is considered a valid therapeutic option for SVT [126].It is our practice to consider the use of anticoagulants (generally LMWH or fondaparinux) as part of the treatment regimen for SVT.

Target-Specific Oral Anticoagulants And Treatment of VTE

Because of warfarin’s narrow therapeutic window, need for frequent monitoring, significant drug and food interactions, and unfavorable kinetics, the target-specific oral anticoagulants (TSOACs) have been developed with the aim of offering alternatives to warfarin therapy (Figure 3). These drugs have been developed to inhibit either thrombin or factor Xa to disrupt the coagulation cascade. Since these drugs bind directly to coagulation factor, they are associated with rapid onset of action, a wide therapeutic window, fewer drug interactions than warfarin, and predictable dose-response allowing for fixed dosing without lab monitoring.

The direct thrombin inhibitor dabigatran directly binds to thrombin in a concentration-dependent manner [127].Peak plasma concentration is achieved within 0.5 to 2.0 hours after ingestion, and its half-life is 12 to 17 hours. Use of dabigatran in both primary and secondary prevention of VTE has been extensively studied, especially in orthopedic surgery where there have been 4 main trials (RE-MOBILIZE, RE-MODEL, RE-NOVATE, and RE-NOVATE I and II). While RE-MOBILIZE showed that dabigatran 220 mg or 150 mg once daily was inferior to enoxaparin 30 mg twice daily in preventing VTE after total knee arthroplasty, RE-MODEL and RE-NOVATE I and II demonstrated that dabigatran 150 mg or 220 mg once daily was noninferior to enoxaparin 40 mg once daily for prevention of VTE in patients undergoing total knee replacement and hip replacement [128–131].The side effect profile was also promising, with no significant differences in the frequency of major bleeding between dabigatran and enoxaparin. Pooled data and meta-analyses from these trials have demonstrated that for prevention of VTE associated with hip or knee surgery, dabigatran 220 mg or 150 mg once daily is as effective as 40 mg of enoxaparin given daily or 30 mg given twice a day, with a similar bleeding profile [132,133].

More recently, dabigatran been used in the acute treatment and secondary prevention of VTE. In the RE-COVER trial, dabigatran 150 mg twice daily was compared to warfarin (INR 2–3) in the treatment of acute VTE for 6 months, after an initial treatment period of up to 9 days with LMWH or UFH. Dabigatran was noninferior to warfarin with respect to 6-month incidence of recurrent symptomatic objectively confirmed VTE and related deaths, and was not associated with increased bleeding [134].In the RE-MEDY and RE-SONATE trials of extended anticoagulation, dabigatran was as effective as warfarin for prevention of recurrent VTE when continued after 3 months of initial anticoagulation and associated with less bleeding, and was more effective than placebo in preventing recurrent VTE but associated with a higher risk of bleeding [135].Unexpectedly, the risk of acute coronary syndrome was slightly higher in the dabigatran group than the warfarin group, as seen in other studies.

Rivaroxaban, a TSOAC that targets factor Xa, has also shown efficacy in preventing VTE after knee or hip surgery. The RE-CORD 1-4 studies all focused on the use of rivaroxaban in comparison to enoxaparin and found that rivaroxaban 10 mg once daily was superior to enoxaparin 40 mg once daily in prevention of VTE in total knee and total hip arthroplasty [136–138].Meta-analysis of multiple rivaroxaban VTE prophylaxis trials also demonstrated that rivaroxaban significantly lowered the risk of VTE in these surgical patients in comparison to the use of enoxaparin [139].Prophylactic use of rivaroxaban was also studied in acutely ill hospitalized patients in the MAGELLAN trial. Rivaroxaban 10 mg daily for 35 days was compared to enoxaparin 40 mg daily for 10 days followed by placebo and was found to be noninferior to enoxaparin in reduction of VTE risk at day 10 and superior to placebo at day 35 [140].However, the rate of bleeding, although low in both arms, was higher in the rivaroxaban arm.

Rivaroxaban has been studied in randomized clinical trials for acute treatment of DVT and PE and for extended prophylaxis for recurrent VTE (EINSTEIN-DVT, EINSTEIN-PE and EINSTEIN-Extension, respectively).  The treatment strategy for use of rivaroxaban differed from that of dabigatran (in the RE-COVER trial), as rivaroxaban was used upfront as initial anticoagulation rather than after an initial period of parenteral therapy with LMWH or UFH. In both the DVT and PE trials, rivaroxaban was noninferior to standard treatment with enoxaparin followed by warfarin therapy, with no significant difference in major bleeding at 6 months of treatment [141,142].The extension trial also demonstrated that use of rivaroxaban in comparison to placebo for an additional 6 or 12 months after standard therapy was associated with significantly fewer recurrent VTE [141]. These studies led to FDA approval for rivaroxaban for primary prevention of VTE in patients undergoing elective total hip or knee repair surgery, for treatment of acute DVT or PE, and for extended prophylaxis in patients following initial treatment.

The anti-factor Xa TSOAC apixaban has been studied in similar fashion as rivaroxaban. In the AMPLIFY study, apixaban was given at a dose of 10 mg twice daily for 7 days followed by 5 mg twice daily for 6 months (as monotherapy, without initial parenteral agent) and compared to enoxaparin followed by warfarin for treatment of acute VTE. Apixaban was as effective as warfarin in terms of recurrent symptomatic VTE or VTE-related death, and was associated with significantly fewer bleeding events [143].Extended-duration apixaban given at treatment dose (5 mg twice daily) or at prophylactic dose (2.5 mg twice daily) for 12 months after completion of treatment-dose apixaban for VTE demonstrated superiority to placebo for extended prophylaxis in AMPLIFY-EXT, and there was no increase in major bleeding compared to placebo [144].Apixaban was recently approved by the FDA for both treatment and secondary prophylaxis of VTE.

More recently, a third anti-factor Xa TSOAC edoxaban demonstrated noninferiority to warfarin in prevention of recurrent symptomatic VTE when administered to patients with DVT or PE at 60 mg once daily for 3 to 12 months [145].Edoxaban also led to significantly less bleeding than warfarin. Edoxaban was recently approved by the FDA for treatment of VTE.

These TSOACs show promise in treatment and prevention of VTE but should be used in patients who meet appropriate criteria for renal function, age, and bleeding risk, as there are currently no available antidotes to reverse their effects. If significant bleeding occurs and cannot be controlled by usual maneuvers such as mechanical compression or surgical intervention, there is little data to guide the use of pharmacologic interventions. Plasma dabigatran levels can be reduced through the use of hemodialysis [146].Antibodies capable of neutralizing dabigatran have been developed, and one specific antibody, idarucizumab, was well-tolerated and showed immediate and complete reversal of dabigatran in subjects of different age and renal function [147,148].Andexanet, a modified recombinant derivative of factor Xa with no catalytic activty, acts as a “decoy receptor” with higher affinity to factor Xa inhibitors than natural factor Xa.  Phase II studies in healthy volunteers demonstrated that andexanet immediately reversed the anticoagulation activity of apixaban, rivaroxaban, enoxaparin, and most recently edoxaban without thrombotic consequences [149].Two randomized, double-blind, placebo-controlled phase III studies (ANNEXA-A, looking at the reversal of apixaban, and ANNEXA-R, looking at reversal of rivaroxaban) are underway, and preliminary results show that a single intravenous bolus of andexanet demonstrated almost complete reversal [150].Finally, aripazine (PER977), a synthetic small molecule that binds to heparins as well as all TSOACs, was shown in a phase II trial to decrease blood clotting time to within 10% above baseline value in 10 minutes or less with an effect lasting for 24 hours [151].

Some have advocated for use of prothrombin complex concentrate (PCC) or recombinant factor VIIa for reversal of TSOAC-associated bleeding. Rivaroxaban was demonstrated to be partially reversible by PCC, whereas this approach was not as successful for dabigatran in healthy volunteers [152].In vitro evidence, however, showed that PCC did not significantly change aPTT [153].At present, the use of nonspecific hemostatic agents (including recombinant factor VIIa, 4-factor prothrombin complex concentrate, and activated prothrombin complex concentrates) is suggested for reversal of TSOACs in patients who present with life-threatening bleeding [154,155].

Conclusion

Patients with VTE present with a wide range of findings and factors that impact management. Decision making in VTE management is a fluid process that should be re-evaluated as new data emerge and individual circumstances change. There is more focus on VTE prevention and treatment today than there was even a decade ago. Diagnostic algorithms, identification of new risk factors, refinement in understanding of the pathogenesis of thrombosis, and identification of new anticoagulants with more favorable risk-benefit profiles will all ultimately contribute to improved patient care.

 

Corresponding author: Jean M. Connors, MD, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02215.

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From the Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA.

 

Abstract

  • Objective: To review the diagnosis and management of venous thromboembolism (VTE).
  • Methods: Review of the literature.
  • Results: VTE and its associated complications account for significant morbidity and mortality. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. Clinical decision rules have been developed  that can help determine which patients warrant further testing. Anticoagulation, the mainstay of VTE treatment, increases bleeding risk, necessitating tailored treatment strategies that must incorporate etiology, risk, benefit, cost, and patient preference.
  • Conclusion: Further study is needed to understand individual patient risks and to identify treatments that will lead to improved patient outcomes.

 

Venous thromboembolism (VTE) and its associated complications account for significant morbidity and mortality. Each year between 100 and 180 persons per 100,000 in Western countries develop VTE. The majority of VTEs are classified as either pulmonary embolism (PE), which accounts for one third of the events, or deep vein thrombosis (DVT), which is responsible for the remaining two thirds. Between 20% and 30% of those patients diagnosed with thrombotic events will die within the first month after diagnosis [1].PE is a common consequence of DVT; 40% of patients who are diagnosed with DVT will be subsequently found to have PE upon further imaging. This high rate of association is also seen in those who present with PE, 70% of whom will also be found to have concomitant DVT [2,3].

There are many risk factors for VTE, including patient-specific demographic factors, environmental factors, and pharmacologic factors (Table 1). One of the main demographic factors associated with development of VTE is age. It is rare for children to suffer a thrombotic event, whereas older persons have a risk of 450 to 600 events per 100,000 [1]. Other demographic risk factors, both inherited and acquired, have been associated with increased risk of VTE. Inherited risk factors include factor V leiden mutation, prothrombin gene mutation, protein C and protein S deficiencies, antithrombin deficiency, and dysfibrinogenemia. The prevalence of these inherited thrombophilias in patients with VTE is about 25% to 35% compared to 10% in controls without VTE [4,5].Acquired risk factors include prior VTE, malignancy, surgery, trauma, obesity, smoking, pregnancy, and immobilization [6–9].Additionally, multiple medical conditions, including the antiphospholipid antibody syndrome, myeloproliferative neoplasms, paroxysmal nocturnal hemoglobinuria, renal disease (particularly nephritic syndrome), liver disease, and inflammatory bowel disease have been shown to increase risk of VTE [10–13].

Anatomic risk factors include Paget-Schroetter syndrome (compression of upper extremity veins due to abnormalities at the thoracic outlet), May-Thurner syndrome (significant compression of the left common iliac vein by the right common iliac artery), and abnormalities of the inferior vena cava [14–16].Medications that are associated with increased risk of VTE include but are not limited to estrogen (both in oral contraceptives as well as hormone replacement therapy) [17,18],the selective estrogen receptor modulator tamoxifen [19],testosterone [20],and glucocorticoids [21].It is important to note that many patients with VTE have more than one acquired risk factor for thrombosis [22],and also that acquired risk factors are more likely to lead to VTE in the setting of underlying inherited thrombophilic conditions [23].

Pathogenesis

Abnormalities in both coagulation factors and the vascular bed are at the core of the pathogenesis of VTE. The multifaceted etiology of thrombosis was first described in 1856 by Virchow, who defined a triad of defects in the vessel wall, platelets, and coagulation proteins [24].Usually the vessel wall is lined with endothelial cells that provide a nonthrombotic surface and limit platelet aggregation through release of prostacyclins and nitric oxide. When the endothelial lining is compromised, the homeostatic surveillance system is disturbed and platelet activation and the coagulation system are initiated. Tissue factor exposure in the damaged area of the vessel leads to activation of the coagulation cascade. Collagen that is present in the area of the wound is also exposed and can activate platelets, which provide the phospholipid surface upon which the coagulation cascade occurs. Platelets initially tether to the exposed collagen through binding of glycoprotein Ib-V-IX in association with von Willebrand factor [25].The thrombus is initiated as more platelets are recruited to exposed collagen of the injured endothelium through aggregation in response to the binding of GPIIIb/IIa with fibrinogen. This process is self-perpetuating as these activated platelets release additional proteins such as adenosine diphosphate (ADP), serotonin, and thromboxane A2, all of which fuel the recruitment and activation of additional platelets [26].

Diagnosis

The key to decreasing the morbidity and mortality associated with VTE is timely diagnosis and early initiation of therapy. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. DVT is usually associated with pain in calf or thigh, unilateral swelling, tenderness, and redness. PE can present as chest pain, shortness of breath, syncope, hemoptysis, and/or cardiac palpitations.

Decision Rules

Clinical decision rules based on signs, symptoms, and risk factors have been developed to estimate the pretest probability of PE or DVT and to help determine which patients warrant further testing. These clinical decision rules include the Wells criteria (separate rules for DVT and PE) [27,28],as well as the Geneva score [29],which is focused on identifying patients with a likelihood of having a PE. In general, these clinical rules are applied at presentation to predict the risk of VTE, and patients who score high are evaluated by imaging modalities, while those with lower scores should be considered for further stratification based on D-dimer testing. The goal of clinical assessment and use of a decision rule is to identify patients at low risk of VTE to reduce the number of imaging studies performed. Most of the decision rules focus on the use of noninvasive evaluations that are easily implemented, including clinical history and presentation, abnormalities in oxygen saturation, chest radiography findings, and electro-cardiography.

D-Dimer Testing

D-dimer testing is at the core of all predictive models for VTE. D-dimer is a fibrin degradation product that is detectable in the blood during active fibrinolysis and occurs after clot formation. The concentration of D-dimer increases in patients with active clot. D-dimer testing is usually performed as a quantitative ELISA or automated turbidometric assay and is highly sensitive (> 95%) in excluding a diagnosis of VTE if results are in the normal range [30].The presence of a normal D-dimer and a low probability based on clinical assessment criteria can be integrated to determine which patients have a low (generally < 99%) likelihood of having VTE [31].It should be noted that other factors can lead to an increased D-dimer, including malignancy, trauma, critical illness, disseminated intravascular coagulation, pregnancy, infection, and postoperative status, which can produce false-positive results and cloud the utility of the test in excluding those at low risk of VTE from undergoing imaging [32–34].Additionally, D-dimer values naturally increase with age and recent work has shown utility of an age-adjusted D-dimer threshold, though this method is not yet widespread in clinical practice [35,36].

Imaging

After application of a clinical prediction rule, the mainstay of diagnosis of VTE is imaging. For DVT the use of ultrasonography is considered the gold standard, with both high sensitivity (89–100%) and specificity (86–100%), especially when the DVT is located proximally [37–39].We generally recommend compression ultrasound starting with the proximal veins but expanding to include the whole leg if the proximal studies are negative [40–42].Other diagnostic options include computed tomography (CT) venography, which is not first line as it is highly invasive and exposes the patient to iodine-based contrast dyes, and magnetic resonance venography (MRV), which offers superb visualization for diagnosis of pelvic vein thrombosis but is limited because of availability and cost issues.

Helical CT pulmonary angiography (CTPA) is the diagnostic test of choice in PE, with high sensitivity (96%) and specificity (95%), and has replaced conventional ventilation perfusion (VQ) scanning or other methods such as magnetic resonance pulmonary angiography in most settings [43,44].CTPA should be avoided in patients who have severe chronic kidney disease or a contrast allergy, and is often avoided in patients who are pregnant due to potential risk of radiation exposure, and in such situations VQ scanning may be employed.

Algorithmic Approach to Workup

Our general practice is to apply the Wells clinical prediction rule (Table 2 for DVT and Table 3 for PE), as this system is likely the most familiar to a large number of clinicians and a score can be obtained promptly but accurately based on easily accessible data from history and exam. We generally use the simplified modified criteria presented in the Tables. Once the clinical prediction rule has been applied, we use 2 risk-based algorithms for further evaluation (Figure 1 and Figure 2) [45,46]. In general, we initially perform a D-dimer test for low-risk patients, while we advocate for prompt imaging in high-risk patients to avoid delays in treatment should VTE be diagnosed. Once a diagnosis of VTE is established, treatment should be started promptly. One exception may be isolated 
distal DVT, where it is reasonable to defer treatment in favor of serial ultrasound testing to rule to rule out proximal extension unless the patient is significantly symptomatic with the distal DVT alone [40].

Of note, there are multiple clinical situations in which the application of a clinical prediction rule followed by D-dimer testing and/or imaging cannot be “standardized” with such algorithms. These include situations where D-dimer may be falsely positive (as above), situations in which alternative imaging strategies should be used to avoid contrast exposure in workup of PE (as above), and workup of suspected upper extremity DVT. Upper extremity ultrasound comprises about 10% of all DVT and frequently occurs in the setting of risk factors such as central venous catheters or pacemakers; specific upper-extremity risk-assessment rules have been developed [47,48].

 D-dimer is generally not as useful in workup of upper extremity DVT (given high prevalence of factors that lead to false-positive DVT) and we generally perform compression ultrasonography up front in patients in whom we have high clinical suspicion for upper extremity DVT. In all such clinical situations above, workup should be individualized in accordance with patient factors and careful physician assessment.

Acute Treatment Options

The first step in treatment is identification of patients who are at high risk of 

VTE-related mortality, especially those with PE and hemodynamic instability (defined as systolic blood pressure < 90 mm Hg or a drop in pressure more than 40 mm Hg for more than 15 minutes in the absence of new-onset arrhythmia, hypovolemia, and sepsis). This patient population should be considered for emergent management with thrombolytic therapy, typically recombinant tissue plasminogen activator (t-PA, alteplase). Thrombolysis should be reserved for those who have not had any surgical procedures in the last 2 weeks, have no evidence of neurosurgical bleeding, and are not at risk of a bleeding diathesis. Patients who present without frank hemodynamic instability but have evidence of right ventricular dysfunction (by echocardiography or biomarkers such as troponin elevation) may be at “intermediate risk” for adverse outcomes and the role of thrombolytics in this population is an area of active investigation [49,50].

In standard cases of DVT and PE without hemodynamic compromise, the current standard of care is to initiate parenteral anticoagulation. The immediate goal of therapy is to treat rapidly with anticoagulants to prevent the thrombus from propagating further and to prevent DVT from embolization to the lungs or other vascular beds. The initial treatment of VTE has been extensively discussed and guidelines have been established with recommendations for initiation of anticoagulation; the American College of Chest Physicians (ACCP) released the 9th edition of their guidelines in 2012 based on consensus agreements derived from primary data [51].

Heparin-based drugs are the mainstay of initial treatment. These drugs act by potentiating antithrombin and therefore inactivating thrombin and other coagulation factors such as Xa. Unfractionated heparin (UFH) can be administered as an initial bolus followed by a continuous infusion with dosing being based on weight and titrated to activated partial thromboplastin time (aPTT) or the anti-factor Xa level. Alternatively, patients may be treated with a low molecular weight heparin (LMWH) administered subcutaneously in fixed weight-adjusted doses, which obviates the need for monitoring in most cases [52].LMWHs work in a similar manner to UFH but have more anti-Xa activity in comparison to anti-thrombin activity. LMWH appears to be more effective than UFH for initial treatment of VTE and has been associated with lower risk of major hemorrhage [53].The options for treatment of VTE have expanded in recent years with the approval of fondparinux, a pentasaccharide specifically targeted to inhibit factor Xa. Fondaparinux has been shown to have similar efficacy to LMWH in patients with DVT [54],and while it has not been evaluated directly against LMWH for initial treatment of PE it has been shown to be at least as effective and safe as UFH [55].

Both LMWH and fondaparinux are cleared renally and therefore have increased bleeding risk in patients with renal impairment. In patients with creatinine clearance of less than 30 mL/min, dose reduction or lengthening of dosing interval may be appropriate. Anti-factor Xa activity can be used as a functional assay to monitor and titrate the level of anticoagulation in patients treated with UFH, LMWH, and fondaparinux. Monitoring is useful in the setting of impaired renal function (as above) in addition to extremes of body weight and pregnancy. When used for monitoring of UFH, the anti-factor Xa activity can be measured at any time during administration with a therapeutic goal range of 0.3–0.7 international units (IU)/mL. When used for LMWH, a “peak” anti-factor Xa should be measured approximately 4 hours after dosing, with therapeutic goals depending on preparation and schedule of treatment but generally between 0.6 to 1.0 IU/mL for twice daily and around 1.0 -2.0 IU/mL for once-daily [56].For patients on dialysis, we generally use intravenous UFH for acute treatment of VTE, though recent work has shown that enoxaparin (doses of 0.4 to 1 mg/kg/day) was as safe as UFH with respect to bleeding and was associated with shorter hospital length of stay [57].For long-term treatment of VTE, warfarin is generally preferred based on clinical experience with this agent, though small studies have suggested that parenteral agents may be useful alternatives to warfarin [58].

In many patients who are clinically stable without significant medical comorbidities, outpatient administration of these medications without hospitalization is considered safe. Patients with DVT are often safe to manage as outpatients unless significant clot burden is present and thrombolysis is being considered. For PE, the pulmonary embolism severity index (PESI) and simplified index (sPESI) may be useful to risk-stratify patients and identify those at low risk of complications who may be suitable for outpatient treatment [59,60].Studies have shown that hemodynamically stable patients who did not require supplemental oxygenation or have contraindications to LMWH therapy were safely managed as outpatients with low risk of recurrent VTE and bleeding [61,62].One exception may be patients with intermediate risk PE, who are hemodynamically stable but have evidence of right ventricular dysfunction and may be better served by an initial in-hospital observation period, especially if thrombolysis is being considered.

Most patients who present with VTE are transitioned to warfarin for long-term therapy. Warfarin can be started on the same day as parenteral anticoagulation. Both drugs are overlapped for at least 5 days, with a target INR of 2.0–3.0. Patients may achieve the target INR level quickly because factor VII has a short half-life and the level drops quickly; however, the overlap of 5 days is essential even when the INR is in the target range because a full anticoagulant affect is not achieved until prothrombin levels decline, and this is a slow process due to the long half-life of prothrombin. Warfarin also causes rapid decrease in levels of natural anticoagulants such as protein C and protein S, which further exacerbates the net hypercoagulable state in the short-term. Warfarin without a bridging parenteral agent carries a risk of warfarin-induced skin necrosis [63]and is not effective as an initial anticoagulant treatment in acute VTE as there is a relatively high risk of symptomatic clot extension or recurrent VTE compared to warfarin with use of a bridging agent [64].In specific cases such as cancer-associated VTE (see discussion below), LMWH is preferred to warfarin for long-term active therapy.

Long-Term Active Therapy After Acute Treatment

Duration of Anticoagulation

Recommended duration of anticoagulation depends on a myriad of factors including severity of VTE, risk of recurrence, bleeding risk, and lifestyle modification issues, as well as on the safety and availability of alternative therapies such as low-intensity warfarin, aspirin, or the new oral anticoagulants. The decision tree for length of treatment starts with whether the VTE was a provoked or a spontaneous event. Provoked events occur when the event is associated with an identifiable risk factor, such as immobilization from prolonged medical illness or surgical intervention, pregnancy or oral contraceptive use, and prolonged air travel.

Consensus guidelines suggest that 3 months of anti-coagulation are generally sufficient treatment for a provoked VTE [51,65,66]. Data from multiple studies and a meta-analysis suggests that less than 3 months of anticoagulation (4 to 6 weeks in most trials) is associated with an approximately 1.5-fold higher risk of recurrent VTE than 3 months [67,68].However, data from this meta-analysis also suggests that anticoagulation for longer than 3 months (6 to 12 months in most trials) is not associated with higher rates of recurrent VTE. We generally anticoagulate for 3 months in patients with provoked VTE.

Determining the duration of anticoagulation is more complex in patients with idiopathic/unprovoked VTE. Kearon and colleagues found that in patients with first idiopathic VTE, patients who were anticoagulated for 24 months versus 3 months had lower risk of recurrent VTE (1.3% per patient-year with 24 months versus 27.4% per patient-year with 3 months) [69].Similar studies and meta-analyses have demonstrated decreased recurrence rates in patients anticoagulated for a prolonged period of time. However, one study of prolonged anticoagulation revealed that at 3 years there was no difference in recurrence rate in patients with PE who were anticoagulated for 6 months versus 1 year [70].The likelihood of recurrent DVT in patients with first episode of idiopathic proximal DVT treated with either 3 months or 12 months of warfarin was similar after treatment was discontinued [71].Prolonged periods of anticoagulation do not directly influence risk of recurrence but instead may only delay occurrence of a second event [72].For that reason, the decision is essentially whether to anticoagulate for 3 months or to continue therapy indefinitely [73]. Current guidelines recommend continuing anticoagulation for 3 months in those at high risk of bleeding, and continuing for an extended duration in those at low or moderate bleeding risk [51]. Patients' values and perferences should be entertained and decisions made on a patient-by-patient basis.

For patients at high risk of recurrent VTE, we generally recommend indefinite anticoagulation unless the patient has a significantly elevated bleeding risk or strongly prefers to discontinue anticoagulation and compliance concerns are evident. High-risk patients are those who have suffered from multiple episodes of recurrent VTE, those who have clotted while being anticoagulated, and those with acquired risk factors, such as antiphospholipid antibodies and malignancy. Other high-risk groups are those with high-risk thrombophilias such as deficiency of protein S, protein C, or antithrombin, homozygous factor V Leiden or prothrombin gene mutations, and compound heterozygous factor V Leiden/prothrombin gene mutation in the setting of an unprovoked event. Further discussion of models for risk assessment of recurrence is provided below.

Assessment of Bleeding Risk

The bleeding risk associated with the use of anticoagulation must be weighed against the risk of clotting events when determining duration of anticoagulation, especially in those patients for whom indefinite anticoagulation is a consideration. Risk of bleeding while on anticoagulation is approximately 1–3% per 100 patient-years [74],but concomitant medical conditions such as renal failure, diabetes-related cerebrovascular disease, malignancy, advanced age, and use of antiplatelet agents all increase the risk of bleeding. Bleeding risk is highest when patients first initiate anticoagulation and is approximately 10 times the risk in the first month of therapy than after the first year of therapy [75].

Risk assessment models such as the RIETE score may be helpful when indefinite anticoagulation is a possibility [76].The RIETE score encompasses 6 risk factors (age > 75 years, recent bleeding, cancer, creatinine level > 1.2 mg/dL, anemia, or PE at baseline) to categorize patients into low risk (0 points, 0.3% risk of bleeding), intermediate risk (1–4 points, 2.6% risk of bleeding) and high risk (> 4 points, 6.2% risk of bleeding) within 3 months of anticoagulant therapy. The ACCP has developed a more extensive list of 17 potential risk factors for bleeding to categorize patients into low risk (no risk factors, 0.8%/year risk of bleeding), intermediate risk (1 risk factor, 1.6%/year risk of bleeding) and high risk (2 or more risk factors, >6.5%/year risk of bleeding) categories [77].The RIETE score is simpler to use but was not developed for assessing risk of bleeding during indefinite therapy, while the ACCP risk categorization predicts a yearly risk and is therefore applicable for long-term risk assessment but is more cumbersome to use. In practice, we generally use a clinical gestalt of a patient’s clinical risk factors (particularly age, renal or hepatic dysfunction, and frequent falls) to assess if they may be at high risk of bleeding and if the risk of indefinite anticoagulation may thus outweigh the potential benefit.

We also note that several scoring systems (HAS-BLED, HEMORR2HAGES, and ATRIA scores) have been developed to predict those at high risk of bleeding on anticoagulation for atrial fibrillation [78–80].These scores generally include similar clinical risk factors to those in the RIETE and ACCP scoring systems. Several studies have compared the HAS-BLED, HEMORR2HAGES, and ATRIA scores and a systematic review and meta-analysis concluded that the HAS-BLED score is recommended, due to increased sensitivity and ease of application [81].However, as these scores have not been validated for anticoagulation in the setting of VTE, we do not use them in this capacity.

Risk Stratification for Recurrent VTE

When predicting risk of recurrent VTE, clinical risk factors including obesity, male gender, and underlying thrombophilia (including the “high risk” inherited thrombophilias identified above) must taken into consideration. Location of the thrombus must also be considered; it has also been demonstrated that patients with DVT involving the iliofemoral veins are at higher risk of recurrence than those without iliac involvement [82].Other factors that may be useful in risk stratification include D-dimer level and ultrasound to search for residual venous thrombosis.

D-dimer Levels

D-dimer levels are one of the more promising methods for assessing the risk of recurrent VTE after cessation of anticoagulation, especially in the case of idiopathic VTE where indefinite anticoagulation should be considered but may pose either risk of bleeding or significant inconvenience to patients. A normal D-dimer measured 1 month after cessation of anticoagulation offers a high negative predictive value for risk of recurrence [83].A number of studies have demonstrated that patients with elevated D-dimer 1 month after anticoagulation cessation are at increased risk for a recurrent event [84–86].Two predictive models that have been developed incorporate D-dimer testing into decision making [87,88].The DASH predictive model relies on the D-dimer result in addition to age, male sex, and use of hormone therapy as a method of risk stratification for recurrent VTE in patients with a first unprovoked event. Using this scoring system, patients with a score of 0 or 1 had a recurrence rate of 3.1%, those with a score of 2 a recurrence rate of 6.4%, and those with a score of 3 or greater a recurrence rate of 12.3%. The authors postulate that by using this assessment scheme they can avoid lifelong anticoagulation in 51% of patients. The Vienna prediction model uses male sex, location of VTE (proximal DVT and PE are at higher risk), and D-dimer level to predict risk of recurrent VTE. This model has recently been updated to include a “dynamic” component to predict risk of recurrence of VTE from multiple random time points [89].

Overall, D-dimer may be useful for risk stratification. We often employ the method of stopping anticoagulation in patients with unprovoked VTE after 3 months (if the patient has no identifiable clinical risk factors that place them at high risk of recurrence) and testing D-dimer 1 month after cessation of anticoagulation. An elevated D-dimer is a solid reason to restart anticoagulation (potentially on an indefinite basis), while a negative D-dimer provides support for withholding further anticoagulation in the absence of other significant risk factors for recurrence. However, lack of agreement regarding assay cut-points as well as multiple reasons other than VTE for D-dimer elevation may limit widespread use of this method. We generally use a cutpoint of 250 ug/L as “negative,” though at least one study showed that cut-points of 250 ug/L versus 500 ug/L did not change the utility of this method [90].In our practice, risk prediction models are most useful to provide patients with additional information and a visual presentation to support our recommendation. This is particularly true of the Vienna prediction rule, which is available in a printable nomogram which can be distributed to patients and completed together during the clinic visit.

Imaging Analysis

Imaging analysis may also assist with risk stratification. Clinical assessment modules have been developed that incorporate repeat imaging studies for assessment of recannulization of affected veins. In patients with residual vein thrombosis (RVT) at the time anticoagulation was stopped, the hazard ratio for recurrence was 2.4 compared to those without RVT [91].There are a number of ways RVT could impact recurrence, including inpaired venous flow leading to stasis and activation of the coagulation cascade. Subsequent studies used serial ultrasound to determine when to stop anticoagulation. In one study, patients were anticoagulated for 3 months and for those that had RVT, anticoagulation was continued for up to 9 months for provoked and 21 months for unprovoked VTE. In comparison to fixed dosing of 6 months of anti-coagulation, those who had their length of anticoagulation tailored to ultrasonography findings had a lower rate of recurrent VTE [92].Limitations to using RVT in clinical decision-making include lack of a standard definition of RVT and variability in both timing of ultrasound (operator variability) and interpretation of results [93].

Other Options

Another option in patients who are being considered for indefinite anticoagulation is to decrease the intensity of anticoagulation. Since this would theoretically lower the risk of bleeding, the perceived benefit of long-term, low-intensity anticoagulation would be reduction in both bleeding and clotting risk. The PREVENT trial randomized patients who had received full-dose anticoagulation for a median of 6.5 months to either low-intensity warfarin (INR goal of 1.5-2.0 instead of 2.0-3.0) or placebo. In the anticoagulation group, there was a 64% risk reduction in recurrent VTE (hazard ratio 0.36, 95% CI 0.19 to 0.67) but an increased risk of bleeding (hazard ratio 1.92, 95% CI 1.26 to 2.93) [94].The ELATE study randomized patients with unprovoked VTE who had completed 3 or more months of full-intensity warfarin therapy (target INR 2.0–3.0) to continue therapy with either low-intensity warfarin (target INR 1.5–2.0) or full-intensity warfarin (target INR 2.0-3.0). Compared to the low-intensity group, the conventional-intensity group had lower rates of recurrent VTE and no increased rates of major bleeding [95].This study, however, has been criticized because of its overall low bleeding rate in both treatment groups.

Aspirin is an option in patients in whom long-term anticoagulation is untenable. The ASPIRE trial demonstrated that in patients with unprovoked VTE who had completed a course of initial anticoagulation, aspirin 100 mg daily reduced the risk of major vascular events compared to placebo with no increase in bleeding [96].However, aspirin was not associated with a significant reduction in risk of VTE alone (only the composite vascular event endpoint). The WARFASA trial, however, demonstrated that aspirin 100 mg daily was associated with a significant reduction in recurrent VTE compared to placebo after 6 to 18 months of anticoagulation without an increase in major bleeding [97].The absolute risk of recurrence was 11% in the placebo group and 5.9% in the aspirin group. More recently, the INSPIRE collaboration analyzed data from both trials and found that aspirin after initial anticoagulation reduced the risk of recurrent VTE by approximately 42% with a low rate of major bleeding [98].The absolute risk reduction was even larger in men and older patients. For this reason, we recommend aspirin to those patients in whom indefinite anticoagulation may be warranted from the standpoint of reducing risk of recurrent VTE but in whom the risk of bleeding precludes its use.

Hypercoagulable States In Specific Populations

Inherited Thrombophilias

Patients with a hereditary thrombophilia are at increased risk for incident VTE [99].These inherited mutations result in either a loss of normal anticoagulant function or gain of a prothrombotic state. Hereditary disorders associated with VTE include deficiency of antithrombin, protein C, or protein S, or the presence of factor V Leiden and/or prothrombin G20210A mutations. Although deficiency of protein C, protein S, or antithrombin is uncommon and affects only 0.5% of the population, these states have been associated with a 10-fold increased risk of thrombosis in comparison to the general population. Factor V Leiden and prothrombin gene mutation are less likely to be associated with incident thrombosis (2 to 5-fold increased risk of VTE) and are more prevalent in the Caucasian population [100].Though these hereditary thrombophilias increase risk of VTE, prophylactic anti-coagulation prior to a first VTE is not generally indicated.

Data regarding the impact of the inherited thrombophilias on risk of recurrent VTE is less well defined. While some data suggest that inherited thrombophilias are associated with increased risk of recurrent VTE, the degree of impact may be clinically modest especially in those with heterozygous factor V Leiden or prothrombin gene mutations [101].Ideally, a clinical trial would be designed to assess whether hereditary thrombophilia testing is beneficial for patients with VTE in decision-making regarding length of anticoagulation, type of anticoagulation, and risk of recurrence. If a patient with a low-risk inherited thrombophilia has a DVT in the setting of an additional provoking risk factor (surgery, pregnancy, etc), a 3-month course of anticoagulation followed by D-dimer assessment as above is reasonable. If a patient with an inherited thrombophilia experiences an idiopathic VTE, or if a patient with a “high-risk” thrombophilia as described above experiences any type of VTE, we generally recommend indefinite anticoagulation in the absence of high bleeding risk, though again this is a very patient-dependent choice.

Acquired Thrombophilias

Antiphospholipid Syndrome

Antibodies directed against proteins that bind phospho-lipids are associated with an acquired hypercoagulable state. The autoantibodies are categorized as antiphospho-lipid antibodies (APLAs), which include anticardiolipin antibodies (IgG and IgM), beta-2 glycoprotein 1 antibodies (anti-B2 GP), and lupus anticoagulant. These antibodies can form autonomously, as seen in primary disorders, or in association with autoimmune disease as a secondary disorder.

Criteria have been developed to distinguish antiphospholipid-associated clotting disorders from other forms of thrombophilia. The updated Sapporo criteria depend on both laboratory and clinical diagnostic criteria [102].The laboratory diagnosis of APLAs requires the presence of lupus anticoagulants, anticardiolipin antibodies, or anti-B2 GP on at least 2 assays at least 12 weeks apart with elevation above the 99th percentile of the testing laboratory’s normal distribution [103].Testing for lupus anticoagulant is based on 3 stages, the first of which is inhibition of phospholipid-dependent coagulation tests with prolonged clotting time (eg, aPTT or dilute Russell’s viper venom time). The diagnosis is confirmed by a secondary test in which excess hexagonal phase phospholipids are added to incubate with the patient’s plasma to absorb the APLA [104].The presence of anticardiolipin antibodies and anti beta-2 GP antibodies is determined using ELISA based immunoassays. Unlike most other thrombophilias, antiphospholipid syndrome is associated with both arterial and venous thromboembolic events and may be an indication for lifelong anticoagulation after a first thrombotic event. We generally recommend indefinite anticoagulation in the absence of significant bleeding risk.

Cancer-Associated Hypercoagulable State

Patients with cancer have a propensity for thromboembolic events. The underlying mechanisms responsible for cancer-associated clotting events are multifactorial and an area of intense research. Tumor cells can initiate activation of the clotting cascade through release of tissue factor and other pro-coagulant molecules [105].Type and stage of cancer impact risk of VTE, and the tumor itself can compress vasculature leading to venous stasis. Furthermore, chemotherapy, hormone therapy, antiangiogenic drugs, erythropoietin agents, and indwelling central venous catheters all are associated with increased risk of thrombotic events. Approximately 25% of all cancer patients will experience a thrombotic event during the course of their disease [106]. In fact, the presence of a spontaneous clot may be a harbinger of underlying malignancy [107].Approximately 10% of patients who present with an idiopathic VTE are diagnosed with cancer in the next 1 to 2 years.

The utility of extensive cancer screening in patients with spontaneous clotting events is often debated. The small studies that have addressed cancer associated clots have not demonstrated any mortality benefit with extensive screening. A prospective cohort study addressed the utility of limited versus extensive screening [108].In this study, all patients underwent a series of basic screening tests such as history taking, physical examination, chest radiograph, and basic laboratory parameters. Approximately half of the patients underwent additional testing (CT of chest and abdomen and mammography for women). Screening did not result in increased survival or fewer cancer-related deaths. 3.5 % of patients in the extensive screening group were diagnosed with malignancy in comparison to 2.4% in the limited screening group. During follow-up, cancer was diagnosed in 3.7% and 5.0% in the extensive and limited screening groups, respectively. The authors concluded that the low yield of extensive screening and lack of survival benefit did not warrant routinely ordering cancer screening tests above and beyond age-appropriate screening in patients with idiopathic VTE. However, it is known that identification of occult malignancy at an earlier stage of disease is beneficial, and cancer diagnosed within one year of an episode of VTE is generally more advanced and associated with a poorer prognosis [109].It is our practice to take a through history from patients with unprovoked clots particularly focusing on symptoms suggestive of an underlying cancer. We recommend that patients be up to date with all age-appropriate cancer screening.

Heparin-based products (rather than warfarin) are recommended for long-term treatment of cancer-associated DVT. Several trials, most prominently the CLOT trial, have demonstrated that LMWH is associated with reduced risk of recurrent VTE compared with warfarin in cancer patients [110].Fondaparinux may be a reasonable alternative if a patient is unable to tolerate a LMWH. In terms of treatment duration, patients with cancer-associated VTE should be anticoagulated indefinitely as long as they continue to have evidence of active malignancy and/or remain on antineoplastic treatment [111].

Heparin has potential anticancer effects beyond its anticoagulation properties. It is believed that heparin use in patients with cancer can influence cancer progression by acting as an antimetastatic agent. The molecular mechanisms underlying this significant observation are not completely understood, although the first documented benefit of these drugs dates back to the 1970s [112].Overall, LMWH have been associated with improved overall survival in cancer patients and this effect appears to be distinct from its ability to prevent life-threatening VTE episodes [113].

Estrogen-Related Thromboembolic Disease

Pregnancy is a well-established acquired hypercoagulable state, and thromboembolic disease accounts for significant morbidity and mortality in pregnancy and the postpartum period. Approximately 1 in 1000 women will suffer from a thrombotic event during pregnancy or shortly after delivery [8]. The etiology of the tendency to clot during pregnancy is multifactorial and mainly reflects venous stasis due to vasculature compression by the uterus, changes in coagulation factors as the pregnancy progresses, and endothelial damage during delivery, especially Cesarean section. Both factor VIII and von Willebrand factor levels increase, especially in the final months of pregnancy. Simultaneously, levels of the natural anticoagulant protein S diminish, leading to an acquired resistance to activated protein C which results in increased thrombin generation and therefore a hypercoagulable state [114].The risk of thrombosis in pregnancy is clearly heightened in women with inherited thrombophilias, especially in the postpartum period [115].

Similarly to pregnancy, hormone-based contraceptive agents and estrogen replacement therapies are also associated with increased thrombotic risk. Over the years, drug manufacturers have tried to mitigate the clotting risk associated with these drugs by reducing the amount of estrogen and altering the type of progesterone used, yet a risk still remains, resulting in a VTE incidence 2 to 7 times higher in this population [116].The risk is highest in the first 4 months of use and is unaffected by duration of use; risk extends for 3 months after cessation of estrogen-containing therapy. Patients who develop VTE while taking an oral contraceptive are generally instructed to stop the contraceptive and consider an alternative form of birth control. Although routine screening for thrombophilia is not offered to women before prescribing oral contraceptives, a thorough personal and family history regarding venous and arterial thrombotic events as well as recurrent pregnancy loss in women should be taken to evaluate thromboembolic risk factors. We generally avoid use of oral contraceptives in patients with a known hereditary thrombophilia, and consider screening prior to initiation of therapy in those with a strong family history of VTE.

Superficial VTE

Although the main disorders that comprise VTE are DVT and PE, another common presentation is superficial venous thromboembolism (SVT). The risk factors for developing an SVT are similar to those for DVT. In addition, varicose veins also increase the incidence of developing SVT [117].SVT is not associated with excessive mortality, and the main concern with it is progression to DVT. About 25% of patients diagnosed with SVT may have DVT or PE at the time of diagnosis and about 3% without DVT or PE at time of diagnosis developed one of these complications over the following 3 months; clot propagation is another common complication [118].Ultrasound may be of utility in diagnosing occult DVT in patients who initially diagnosed with SVT [119].

For patients who have only SVT at baseline without concomitant DVT or PE, it is difficult to determine which patients are at risk for developing DVT. Some risk stratification models include clot location. Since SVT clots usually develop in the saphenous vein, the clot would need to either progress from the sapheno-femoral junction to the common femoral vein; thus, any clots located near the sapheno-femoral junction are at risk of progressing into the deep vasculature [120].Clots within 3 cm of the junction may be more likely to progress to DVT [121].Chengelis and colleagues feel that proximal saphenous vein thrombosis should likely be treated with anticoagulation [122].Others have taken a more general approach, stating that all clots above the knee or in the thigh area should be treated aggressively [123].

There are solid data for the use of anticoagulation in SVT. In the STEFLUX (Superficial ThromboEmbolism and Fluxum) study, participants received the LMWH parnaparin at one of 3 doses: 8500 IU once daily for 10 days followed by placebo for 20 days, 8500 IU once daily for 10 days and then 6400 IU once daily for 20 days, or 4250 IU once daily for 30 days. Those who received the intermediate dosing had lower rates of DVT, PE, and relapse/SVT recurrence in the first 33 days [124].In the CALISTO trial, fondaparinux 2.5mg per day for 45 days effectively reduced the risk of symptomatic DVT, PE, or SVT recurrence or extension and was not associated with any increased major bleeding compared to placebo [125].A Cochrane review included 30 studies involving over 6500 participants with SVT of the lower extremities. The treatments used in these studies included fondaparinux, LMWH, UFH, non-steriodal anti-inflammatory agents, topical treatment, and surgery. According to the findings, use of fondaparinux at prophylactic dosing for 6 weeks is considered a valid therapeutic option for SVT [126].It is our practice to consider the use of anticoagulants (generally LMWH or fondaparinux) as part of the treatment regimen for SVT.

Target-Specific Oral Anticoagulants And Treatment of VTE

Because of warfarin’s narrow therapeutic window, need for frequent monitoring, significant drug and food interactions, and unfavorable kinetics, the target-specific oral anticoagulants (TSOACs) have been developed with the aim of offering alternatives to warfarin therapy (Figure 3). These drugs have been developed to inhibit either thrombin or factor Xa to disrupt the coagulation cascade. Since these drugs bind directly to coagulation factor, they are associated with rapid onset of action, a wide therapeutic window, fewer drug interactions than warfarin, and predictable dose-response allowing for fixed dosing without lab monitoring.

The direct thrombin inhibitor dabigatran directly binds to thrombin in a concentration-dependent manner [127].Peak plasma concentration is achieved within 0.5 to 2.0 hours after ingestion, and its half-life is 12 to 17 hours. Use of dabigatran in both primary and secondary prevention of VTE has been extensively studied, especially in orthopedic surgery where there have been 4 main trials (RE-MOBILIZE, RE-MODEL, RE-NOVATE, and RE-NOVATE I and II). While RE-MOBILIZE showed that dabigatran 220 mg or 150 mg once daily was inferior to enoxaparin 30 mg twice daily in preventing VTE after total knee arthroplasty, RE-MODEL and RE-NOVATE I and II demonstrated that dabigatran 150 mg or 220 mg once daily was noninferior to enoxaparin 40 mg once daily for prevention of VTE in patients undergoing total knee replacement and hip replacement [128–131].The side effect profile was also promising, with no significant differences in the frequency of major bleeding between dabigatran and enoxaparin. Pooled data and meta-analyses from these trials have demonstrated that for prevention of VTE associated with hip or knee surgery, dabigatran 220 mg or 150 mg once daily is as effective as 40 mg of enoxaparin given daily or 30 mg given twice a day, with a similar bleeding profile [132,133].

More recently, dabigatran been used in the acute treatment and secondary prevention of VTE. In the RE-COVER trial, dabigatran 150 mg twice daily was compared to warfarin (INR 2–3) in the treatment of acute VTE for 6 months, after an initial treatment period of up to 9 days with LMWH or UFH. Dabigatran was noninferior to warfarin with respect to 6-month incidence of recurrent symptomatic objectively confirmed VTE and related deaths, and was not associated with increased bleeding [134].In the RE-MEDY and RE-SONATE trials of extended anticoagulation, dabigatran was as effective as warfarin for prevention of recurrent VTE when continued after 3 months of initial anticoagulation and associated with less bleeding, and was more effective than placebo in preventing recurrent VTE but associated with a higher risk of bleeding [135].Unexpectedly, the risk of acute coronary syndrome was slightly higher in the dabigatran group than the warfarin group, as seen in other studies.

Rivaroxaban, a TSOAC that targets factor Xa, has also shown efficacy in preventing VTE after knee or hip surgery. The RE-CORD 1-4 studies all focused on the use of rivaroxaban in comparison to enoxaparin and found that rivaroxaban 10 mg once daily was superior to enoxaparin 40 mg once daily in prevention of VTE in total knee and total hip arthroplasty [136–138].Meta-analysis of multiple rivaroxaban VTE prophylaxis trials also demonstrated that rivaroxaban significantly lowered the risk of VTE in these surgical patients in comparison to the use of enoxaparin [139].Prophylactic use of rivaroxaban was also studied in acutely ill hospitalized patients in the MAGELLAN trial. Rivaroxaban 10 mg daily for 35 days was compared to enoxaparin 40 mg daily for 10 days followed by placebo and was found to be noninferior to enoxaparin in reduction of VTE risk at day 10 and superior to placebo at day 35 [140].However, the rate of bleeding, although low in both arms, was higher in the rivaroxaban arm.

Rivaroxaban has been studied in randomized clinical trials for acute treatment of DVT and PE and for extended prophylaxis for recurrent VTE (EINSTEIN-DVT, EINSTEIN-PE and EINSTEIN-Extension, respectively).  The treatment strategy for use of rivaroxaban differed from that of dabigatran (in the RE-COVER trial), as rivaroxaban was used upfront as initial anticoagulation rather than after an initial period of parenteral therapy with LMWH or UFH. In both the DVT and PE trials, rivaroxaban was noninferior to standard treatment with enoxaparin followed by warfarin therapy, with no significant difference in major bleeding at 6 months of treatment [141,142].The extension trial also demonstrated that use of rivaroxaban in comparison to placebo for an additional 6 or 12 months after standard therapy was associated with significantly fewer recurrent VTE [141]. These studies led to FDA approval for rivaroxaban for primary prevention of VTE in patients undergoing elective total hip or knee repair surgery, for treatment of acute DVT or PE, and for extended prophylaxis in patients following initial treatment.

The anti-factor Xa TSOAC apixaban has been studied in similar fashion as rivaroxaban. In the AMPLIFY study, apixaban was given at a dose of 10 mg twice daily for 7 days followed by 5 mg twice daily for 6 months (as monotherapy, without initial parenteral agent) and compared to enoxaparin followed by warfarin for treatment of acute VTE. Apixaban was as effective as warfarin in terms of recurrent symptomatic VTE or VTE-related death, and was associated with significantly fewer bleeding events [143].Extended-duration apixaban given at treatment dose (5 mg twice daily) or at prophylactic dose (2.5 mg twice daily) for 12 months after completion of treatment-dose apixaban for VTE demonstrated superiority to placebo for extended prophylaxis in AMPLIFY-EXT, and there was no increase in major bleeding compared to placebo [144].Apixaban was recently approved by the FDA for both treatment and secondary prophylaxis of VTE.

More recently, a third anti-factor Xa TSOAC edoxaban demonstrated noninferiority to warfarin in prevention of recurrent symptomatic VTE when administered to patients with DVT or PE at 60 mg once daily for 3 to 12 months [145].Edoxaban also led to significantly less bleeding than warfarin. Edoxaban was recently approved by the FDA for treatment of VTE.

These TSOACs show promise in treatment and prevention of VTE but should be used in patients who meet appropriate criteria for renal function, age, and bleeding risk, as there are currently no available antidotes to reverse their effects. If significant bleeding occurs and cannot be controlled by usual maneuvers such as mechanical compression or surgical intervention, there is little data to guide the use of pharmacologic interventions. Plasma dabigatran levels can be reduced through the use of hemodialysis [146].Antibodies capable of neutralizing dabigatran have been developed, and one specific antibody, idarucizumab, was well-tolerated and showed immediate and complete reversal of dabigatran in subjects of different age and renal function [147,148].Andexanet, a modified recombinant derivative of factor Xa with no catalytic activty, acts as a “decoy receptor” with higher affinity to factor Xa inhibitors than natural factor Xa.  Phase II studies in healthy volunteers demonstrated that andexanet immediately reversed the anticoagulation activity of apixaban, rivaroxaban, enoxaparin, and most recently edoxaban without thrombotic consequences [149].Two randomized, double-blind, placebo-controlled phase III studies (ANNEXA-A, looking at the reversal of apixaban, and ANNEXA-R, looking at reversal of rivaroxaban) are underway, and preliminary results show that a single intravenous bolus of andexanet demonstrated almost complete reversal [150].Finally, aripazine (PER977), a synthetic small molecule that binds to heparins as well as all TSOACs, was shown in a phase II trial to decrease blood clotting time to within 10% above baseline value in 10 minutes or less with an effect lasting for 24 hours [151].

Some have advocated for use of prothrombin complex concentrate (PCC) or recombinant factor VIIa for reversal of TSOAC-associated bleeding. Rivaroxaban was demonstrated to be partially reversible by PCC, whereas this approach was not as successful for dabigatran in healthy volunteers [152].In vitro evidence, however, showed that PCC did not significantly change aPTT [153].At present, the use of nonspecific hemostatic agents (including recombinant factor VIIa, 4-factor prothrombin complex concentrate, and activated prothrombin complex concentrates) is suggested for reversal of TSOACs in patients who present with life-threatening bleeding [154,155].

Conclusion

Patients with VTE present with a wide range of findings and factors that impact management. Decision making in VTE management is a fluid process that should be re-evaluated as new data emerge and individual circumstances change. There is more focus on VTE prevention and treatment today than there was even a decade ago. Diagnostic algorithms, identification of new risk factors, refinement in understanding of the pathogenesis of thrombosis, and identification of new anticoagulants with more favorable risk-benefit profiles will all ultimately contribute to improved patient care.

 

Corresponding author: Jean M. Connors, MD, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02215.

From the Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA.

 

Abstract

  • Objective: To review the diagnosis and management of venous thromboembolism (VTE).
  • Methods: Review of the literature.
  • Results: VTE and its associated complications account for significant morbidity and mortality. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. Clinical decision rules have been developed  that can help determine which patients warrant further testing. Anticoagulation, the mainstay of VTE treatment, increases bleeding risk, necessitating tailored treatment strategies that must incorporate etiology, risk, benefit, cost, and patient preference.
  • Conclusion: Further study is needed to understand individual patient risks and to identify treatments that will lead to improved patient outcomes.

 

Venous thromboembolism (VTE) and its associated complications account for significant morbidity and mortality. Each year between 100 and 180 persons per 100,000 in Western countries develop VTE. The majority of VTEs are classified as either pulmonary embolism (PE), which accounts for one third of the events, or deep vein thrombosis (DVT), which is responsible for the remaining two thirds. Between 20% and 30% of those patients diagnosed with thrombotic events will die within the first month after diagnosis [1].PE is a common consequence of DVT; 40% of patients who are diagnosed with DVT will be subsequently found to have PE upon further imaging. This high rate of association is also seen in those who present with PE, 70% of whom will also be found to have concomitant DVT [2,3].

There are many risk factors for VTE, including patient-specific demographic factors, environmental factors, and pharmacologic factors (Table 1). One of the main demographic factors associated with development of VTE is age. It is rare for children to suffer a thrombotic event, whereas older persons have a risk of 450 to 600 events per 100,000 [1]. Other demographic risk factors, both inherited and acquired, have been associated with increased risk of VTE. Inherited risk factors include factor V leiden mutation, prothrombin gene mutation, protein C and protein S deficiencies, antithrombin deficiency, and dysfibrinogenemia. The prevalence of these inherited thrombophilias in patients with VTE is about 25% to 35% compared to 10% in controls without VTE [4,5].Acquired risk factors include prior VTE, malignancy, surgery, trauma, obesity, smoking, pregnancy, and immobilization [6–9].Additionally, multiple medical conditions, including the antiphospholipid antibody syndrome, myeloproliferative neoplasms, paroxysmal nocturnal hemoglobinuria, renal disease (particularly nephritic syndrome), liver disease, and inflammatory bowel disease have been shown to increase risk of VTE [10–13].

Anatomic risk factors include Paget-Schroetter syndrome (compression of upper extremity veins due to abnormalities at the thoracic outlet), May-Thurner syndrome (significant compression of the left common iliac vein by the right common iliac artery), and abnormalities of the inferior vena cava [14–16].Medications that are associated with increased risk of VTE include but are not limited to estrogen (both in oral contraceptives as well as hormone replacement therapy) [17,18],the selective estrogen receptor modulator tamoxifen [19],testosterone [20],and glucocorticoids [21].It is important to note that many patients with VTE have more than one acquired risk factor for thrombosis [22],and also that acquired risk factors are more likely to lead to VTE in the setting of underlying inherited thrombophilic conditions [23].

Pathogenesis

Abnormalities in both coagulation factors and the vascular bed are at the core of the pathogenesis of VTE. The multifaceted etiology of thrombosis was first described in 1856 by Virchow, who defined a triad of defects in the vessel wall, platelets, and coagulation proteins [24].Usually the vessel wall is lined with endothelial cells that provide a nonthrombotic surface and limit platelet aggregation through release of prostacyclins and nitric oxide. When the endothelial lining is compromised, the homeostatic surveillance system is disturbed and platelet activation and the coagulation system are initiated. Tissue factor exposure in the damaged area of the vessel leads to activation of the coagulation cascade. Collagen that is present in the area of the wound is also exposed and can activate platelets, which provide the phospholipid surface upon which the coagulation cascade occurs. Platelets initially tether to the exposed collagen through binding of glycoprotein Ib-V-IX in association with von Willebrand factor [25].The thrombus is initiated as more platelets are recruited to exposed collagen of the injured endothelium through aggregation in response to the binding of GPIIIb/IIa with fibrinogen. This process is self-perpetuating as these activated platelets release additional proteins such as adenosine diphosphate (ADP), serotonin, and thromboxane A2, all of which fuel the recruitment and activation of additional platelets [26].

Diagnosis

The key to decreasing the morbidity and mortality associated with VTE is timely diagnosis and early initiation of therapy. Various imaging modalities can be employed to support a diagnosis of a VTE and are used based on clinical suspicion arising from the presence of signs and symptoms. DVT is usually associated with pain in calf or thigh, unilateral swelling, tenderness, and redness. PE can present as chest pain, shortness of breath, syncope, hemoptysis, and/or cardiac palpitations.

Decision Rules

Clinical decision rules based on signs, symptoms, and risk factors have been developed to estimate the pretest probability of PE or DVT and to help determine which patients warrant further testing. These clinical decision rules include the Wells criteria (separate rules for DVT and PE) [27,28],as well as the Geneva score [29],which is focused on identifying patients with a likelihood of having a PE. In general, these clinical rules are applied at presentation to predict the risk of VTE, and patients who score high are evaluated by imaging modalities, while those with lower scores should be considered for further stratification based on D-dimer testing. The goal of clinical assessment and use of a decision rule is to identify patients at low risk of VTE to reduce the number of imaging studies performed. Most of the decision rules focus on the use of noninvasive evaluations that are easily implemented, including clinical history and presentation, abnormalities in oxygen saturation, chest radiography findings, and electro-cardiography.

D-Dimer Testing

D-dimer testing is at the core of all predictive models for VTE. D-dimer is a fibrin degradation product that is detectable in the blood during active fibrinolysis and occurs after clot formation. The concentration of D-dimer increases in patients with active clot. D-dimer testing is usually performed as a quantitative ELISA or automated turbidometric assay and is highly sensitive (> 95%) in excluding a diagnosis of VTE if results are in the normal range [30].The presence of a normal D-dimer and a low probability based on clinical assessment criteria can be integrated to determine which patients have a low (generally < 99%) likelihood of having VTE [31].It should be noted that other factors can lead to an increased D-dimer, including malignancy, trauma, critical illness, disseminated intravascular coagulation, pregnancy, infection, and postoperative status, which can produce false-positive results and cloud the utility of the test in excluding those at low risk of VTE from undergoing imaging [32–34].Additionally, D-dimer values naturally increase with age and recent work has shown utility of an age-adjusted D-dimer threshold, though this method is not yet widespread in clinical practice [35,36].

Imaging

After application of a clinical prediction rule, the mainstay of diagnosis of VTE is imaging. For DVT the use of ultrasonography is considered the gold standard, with both high sensitivity (89–100%) and specificity (86–100%), especially when the DVT is located proximally [37–39].We generally recommend compression ultrasound starting with the proximal veins but expanding to include the whole leg if the proximal studies are negative [40–42].Other diagnostic options include computed tomography (CT) venography, which is not first line as it is highly invasive and exposes the patient to iodine-based contrast dyes, and magnetic resonance venography (MRV), which offers superb visualization for diagnosis of pelvic vein thrombosis but is limited because of availability and cost issues.

Helical CT pulmonary angiography (CTPA) is the diagnostic test of choice in PE, with high sensitivity (96%) and specificity (95%), and has replaced conventional ventilation perfusion (VQ) scanning or other methods such as magnetic resonance pulmonary angiography in most settings [43,44].CTPA should be avoided in patients who have severe chronic kidney disease or a contrast allergy, and is often avoided in patients who are pregnant due to potential risk of radiation exposure, and in such situations VQ scanning may be employed.

Algorithmic Approach to Workup

Our general practice is to apply the Wells clinical prediction rule (Table 2 for DVT and Table 3 for PE), as this system is likely the most familiar to a large number of clinicians and a score can be obtained promptly but accurately based on easily accessible data from history and exam. We generally use the simplified modified criteria presented in the Tables. Once the clinical prediction rule has been applied, we use 2 risk-based algorithms for further evaluation (Figure 1 and Figure 2) [45,46]. In general, we initially perform a D-dimer test for low-risk patients, while we advocate for prompt imaging in high-risk patients to avoid delays in treatment should VTE be diagnosed. Once a diagnosis of VTE is established, treatment should be started promptly. One exception may be isolated 
distal DVT, where it is reasonable to defer treatment in favor of serial ultrasound testing to rule to rule out proximal extension unless the patient is significantly symptomatic with the distal DVT alone [40].

Of note, there are multiple clinical situations in which the application of a clinical prediction rule followed by D-dimer testing and/or imaging cannot be “standardized” with such algorithms. These include situations where D-dimer may be falsely positive (as above), situations in which alternative imaging strategies should be used to avoid contrast exposure in workup of PE (as above), and workup of suspected upper extremity DVT. Upper extremity ultrasound comprises about 10% of all DVT and frequently occurs in the setting of risk factors such as central venous catheters or pacemakers; specific upper-extremity risk-assessment rules have been developed [47,48].

 D-dimer is generally not as useful in workup of upper extremity DVT (given high prevalence of factors that lead to false-positive DVT) and we generally perform compression ultrasonography up front in patients in whom we have high clinical suspicion for upper extremity DVT. In all such clinical situations above, workup should be individualized in accordance with patient factors and careful physician assessment.

Acute Treatment Options

The first step in treatment is identification of patients who are at high risk of 

VTE-related mortality, especially those with PE and hemodynamic instability (defined as systolic blood pressure < 90 mm Hg or a drop in pressure more than 40 mm Hg for more than 15 minutes in the absence of new-onset arrhythmia, hypovolemia, and sepsis). This patient population should be considered for emergent management with thrombolytic therapy, typically recombinant tissue plasminogen activator (t-PA, alteplase). Thrombolysis should be reserved for those who have not had any surgical procedures in the last 2 weeks, have no evidence of neurosurgical bleeding, and are not at risk of a bleeding diathesis. Patients who present without frank hemodynamic instability but have evidence of right ventricular dysfunction (by echocardiography or biomarkers such as troponin elevation) may be at “intermediate risk” for adverse outcomes and the role of thrombolytics in this population is an area of active investigation [49,50].

In standard cases of DVT and PE without hemodynamic compromise, the current standard of care is to initiate parenteral anticoagulation. The immediate goal of therapy is to treat rapidly with anticoagulants to prevent the thrombus from propagating further and to prevent DVT from embolization to the lungs or other vascular beds. The initial treatment of VTE has been extensively discussed and guidelines have been established with recommendations for initiation of anticoagulation; the American College of Chest Physicians (ACCP) released the 9th edition of their guidelines in 2012 based on consensus agreements derived from primary data [51].

Heparin-based drugs are the mainstay of initial treatment. These drugs act by potentiating antithrombin and therefore inactivating thrombin and other coagulation factors such as Xa. Unfractionated heparin (UFH) can be administered as an initial bolus followed by a continuous infusion with dosing being based on weight and titrated to activated partial thromboplastin time (aPTT) or the anti-factor Xa level. Alternatively, patients may be treated with a low molecular weight heparin (LMWH) administered subcutaneously in fixed weight-adjusted doses, which obviates the need for monitoring in most cases [52].LMWHs work in a similar manner to UFH but have more anti-Xa activity in comparison to anti-thrombin activity. LMWH appears to be more effective than UFH for initial treatment of VTE and has been associated with lower risk of major hemorrhage [53].The options for treatment of VTE have expanded in recent years with the approval of fondparinux, a pentasaccharide specifically targeted to inhibit factor Xa. Fondaparinux has been shown to have similar efficacy to LMWH in patients with DVT [54],and while it has not been evaluated directly against LMWH for initial treatment of PE it has been shown to be at least as effective and safe as UFH [55].

Both LMWH and fondaparinux are cleared renally and therefore have increased bleeding risk in patients with renal impairment. In patients with creatinine clearance of less than 30 mL/min, dose reduction or lengthening of dosing interval may be appropriate. Anti-factor Xa activity can be used as a functional assay to monitor and titrate the level of anticoagulation in patients treated with UFH, LMWH, and fondaparinux. Monitoring is useful in the setting of impaired renal function (as above) in addition to extremes of body weight and pregnancy. When used for monitoring of UFH, the anti-factor Xa activity can be measured at any time during administration with a therapeutic goal range of 0.3–0.7 international units (IU)/mL. When used for LMWH, a “peak” anti-factor Xa should be measured approximately 4 hours after dosing, with therapeutic goals depending on preparation and schedule of treatment but generally between 0.6 to 1.0 IU/mL for twice daily and around 1.0 -2.0 IU/mL for once-daily [56].For patients on dialysis, we generally use intravenous UFH for acute treatment of VTE, though recent work has shown that enoxaparin (doses of 0.4 to 1 mg/kg/day) was as safe as UFH with respect to bleeding and was associated with shorter hospital length of stay [57].For long-term treatment of VTE, warfarin is generally preferred based on clinical experience with this agent, though small studies have suggested that parenteral agents may be useful alternatives to warfarin [58].

In many patients who are clinically stable without significant medical comorbidities, outpatient administration of these medications without hospitalization is considered safe. Patients with DVT are often safe to manage as outpatients unless significant clot burden is present and thrombolysis is being considered. For PE, the pulmonary embolism severity index (PESI) and simplified index (sPESI) may be useful to risk-stratify patients and identify those at low risk of complications who may be suitable for outpatient treatment [59,60].Studies have shown that hemodynamically stable patients who did not require supplemental oxygenation or have contraindications to LMWH therapy were safely managed as outpatients with low risk of recurrent VTE and bleeding [61,62].One exception may be patients with intermediate risk PE, who are hemodynamically stable but have evidence of right ventricular dysfunction and may be better served by an initial in-hospital observation period, especially if thrombolysis is being considered.

Most patients who present with VTE are transitioned to warfarin for long-term therapy. Warfarin can be started on the same day as parenteral anticoagulation. Both drugs are overlapped for at least 5 days, with a target INR of 2.0–3.0. Patients may achieve the target INR level quickly because factor VII has a short half-life and the level drops quickly; however, the overlap of 5 days is essential even when the INR is in the target range because a full anticoagulant affect is not achieved until prothrombin levels decline, and this is a slow process due to the long half-life of prothrombin. Warfarin also causes rapid decrease in levels of natural anticoagulants such as protein C and protein S, which further exacerbates the net hypercoagulable state in the short-term. Warfarin without a bridging parenteral agent carries a risk of warfarin-induced skin necrosis [63]and is not effective as an initial anticoagulant treatment in acute VTE as there is a relatively high risk of symptomatic clot extension or recurrent VTE compared to warfarin with use of a bridging agent [64].In specific cases such as cancer-associated VTE (see discussion below), LMWH is preferred to warfarin for long-term active therapy.

Long-Term Active Therapy After Acute Treatment

Duration of Anticoagulation

Recommended duration of anticoagulation depends on a myriad of factors including severity of VTE, risk of recurrence, bleeding risk, and lifestyle modification issues, as well as on the safety and availability of alternative therapies such as low-intensity warfarin, aspirin, or the new oral anticoagulants. The decision tree for length of treatment starts with whether the VTE was a provoked or a spontaneous event. Provoked events occur when the event is associated with an identifiable risk factor, such as immobilization from prolonged medical illness or surgical intervention, pregnancy or oral contraceptive use, and prolonged air travel.

Consensus guidelines suggest that 3 months of anti-coagulation are generally sufficient treatment for a provoked VTE [51,65,66]. Data from multiple studies and a meta-analysis suggests that less than 3 months of anticoagulation (4 to 6 weeks in most trials) is associated with an approximately 1.5-fold higher risk of recurrent VTE than 3 months [67,68].However, data from this meta-analysis also suggests that anticoagulation for longer than 3 months (6 to 12 months in most trials) is not associated with higher rates of recurrent VTE. We generally anticoagulate for 3 months in patients with provoked VTE.

Determining the duration of anticoagulation is more complex in patients with idiopathic/unprovoked VTE. Kearon and colleagues found that in patients with first idiopathic VTE, patients who were anticoagulated for 24 months versus 3 months had lower risk of recurrent VTE (1.3% per patient-year with 24 months versus 27.4% per patient-year with 3 months) [69].Similar studies and meta-analyses have demonstrated decreased recurrence rates in patients anticoagulated for a prolonged period of time. However, one study of prolonged anticoagulation revealed that at 3 years there was no difference in recurrence rate in patients with PE who were anticoagulated for 6 months versus 1 year [70].The likelihood of recurrent DVT in patients with first episode of idiopathic proximal DVT treated with either 3 months or 12 months of warfarin was similar after treatment was discontinued [71].Prolonged periods of anticoagulation do not directly influence risk of recurrence but instead may only delay occurrence of a second event [72].For that reason, the decision is essentially whether to anticoagulate for 3 months or to continue therapy indefinitely [73]. Current guidelines recommend continuing anticoagulation for 3 months in those at high risk of bleeding, and continuing for an extended duration in those at low or moderate bleeding risk [51]. Patients' values and perferences should be entertained and decisions made on a patient-by-patient basis.

For patients at high risk of recurrent VTE, we generally recommend indefinite anticoagulation unless the patient has a significantly elevated bleeding risk or strongly prefers to discontinue anticoagulation and compliance concerns are evident. High-risk patients are those who have suffered from multiple episodes of recurrent VTE, those who have clotted while being anticoagulated, and those with acquired risk factors, such as antiphospholipid antibodies and malignancy. Other high-risk groups are those with high-risk thrombophilias such as deficiency of protein S, protein C, or antithrombin, homozygous factor V Leiden or prothrombin gene mutations, and compound heterozygous factor V Leiden/prothrombin gene mutation in the setting of an unprovoked event. Further discussion of models for risk assessment of recurrence is provided below.

Assessment of Bleeding Risk

The bleeding risk associated with the use of anticoagulation must be weighed against the risk of clotting events when determining duration of anticoagulation, especially in those patients for whom indefinite anticoagulation is a consideration. Risk of bleeding while on anticoagulation is approximately 1–3% per 100 patient-years [74],but concomitant medical conditions such as renal failure, diabetes-related cerebrovascular disease, malignancy, advanced age, and use of antiplatelet agents all increase the risk of bleeding. Bleeding risk is highest when patients first initiate anticoagulation and is approximately 10 times the risk in the first month of therapy than after the first year of therapy [75].

Risk assessment models such as the RIETE score may be helpful when indefinite anticoagulation is a possibility [76].The RIETE score encompasses 6 risk factors (age > 75 years, recent bleeding, cancer, creatinine level > 1.2 mg/dL, anemia, or PE at baseline) to categorize patients into low risk (0 points, 0.3% risk of bleeding), intermediate risk (1–4 points, 2.6% risk of bleeding) and high risk (> 4 points, 6.2% risk of bleeding) within 3 months of anticoagulant therapy. The ACCP has developed a more extensive list of 17 potential risk factors for bleeding to categorize patients into low risk (no risk factors, 0.8%/year risk of bleeding), intermediate risk (1 risk factor, 1.6%/year risk of bleeding) and high risk (2 or more risk factors, >6.5%/year risk of bleeding) categories [77].The RIETE score is simpler to use but was not developed for assessing risk of bleeding during indefinite therapy, while the ACCP risk categorization predicts a yearly risk and is therefore applicable for long-term risk assessment but is more cumbersome to use. In practice, we generally use a clinical gestalt of a patient’s clinical risk factors (particularly age, renal or hepatic dysfunction, and frequent falls) to assess if they may be at high risk of bleeding and if the risk of indefinite anticoagulation may thus outweigh the potential benefit.

We also note that several scoring systems (HAS-BLED, HEMORR2HAGES, and ATRIA scores) have been developed to predict those at high risk of bleeding on anticoagulation for atrial fibrillation [78–80].These scores generally include similar clinical risk factors to those in the RIETE and ACCP scoring systems. Several studies have compared the HAS-BLED, HEMORR2HAGES, and ATRIA scores and a systematic review and meta-analysis concluded that the HAS-BLED score is recommended, due to increased sensitivity and ease of application [81].However, as these scores have not been validated for anticoagulation in the setting of VTE, we do not use them in this capacity.

Risk Stratification for Recurrent VTE

When predicting risk of recurrent VTE, clinical risk factors including obesity, male gender, and underlying thrombophilia (including the “high risk” inherited thrombophilias identified above) must taken into consideration. Location of the thrombus must also be considered; it has also been demonstrated that patients with DVT involving the iliofemoral veins are at higher risk of recurrence than those without iliac involvement [82].Other factors that may be useful in risk stratification include D-dimer level and ultrasound to search for residual venous thrombosis.

D-dimer Levels

D-dimer levels are one of the more promising methods for assessing the risk of recurrent VTE after cessation of anticoagulation, especially in the case of idiopathic VTE where indefinite anticoagulation should be considered but may pose either risk of bleeding or significant inconvenience to patients. A normal D-dimer measured 1 month after cessation of anticoagulation offers a high negative predictive value for risk of recurrence [83].A number of studies have demonstrated that patients with elevated D-dimer 1 month after anticoagulation cessation are at increased risk for a recurrent event [84–86].Two predictive models that have been developed incorporate D-dimer testing into decision making [87,88].The DASH predictive model relies on the D-dimer result in addition to age, male sex, and use of hormone therapy as a method of risk stratification for recurrent VTE in patients with a first unprovoked event. Using this scoring system, patients with a score of 0 or 1 had a recurrence rate of 3.1%, those with a score of 2 a recurrence rate of 6.4%, and those with a score of 3 or greater a recurrence rate of 12.3%. The authors postulate that by using this assessment scheme they can avoid lifelong anticoagulation in 51% of patients. The Vienna prediction model uses male sex, location of VTE (proximal DVT and PE are at higher risk), and D-dimer level to predict risk of recurrent VTE. This model has recently been updated to include a “dynamic” component to predict risk of recurrence of VTE from multiple random time points [89].

Overall, D-dimer may be useful for risk stratification. We often employ the method of stopping anticoagulation in patients with unprovoked VTE after 3 months (if the patient has no identifiable clinical risk factors that place them at high risk of recurrence) and testing D-dimer 1 month after cessation of anticoagulation. An elevated D-dimer is a solid reason to restart anticoagulation (potentially on an indefinite basis), while a negative D-dimer provides support for withholding further anticoagulation in the absence of other significant risk factors for recurrence. However, lack of agreement regarding assay cut-points as well as multiple reasons other than VTE for D-dimer elevation may limit widespread use of this method. We generally use a cutpoint of 250 ug/L as “negative,” though at least one study showed that cut-points of 250 ug/L versus 500 ug/L did not change the utility of this method [90].In our practice, risk prediction models are most useful to provide patients with additional information and a visual presentation to support our recommendation. This is particularly true of the Vienna prediction rule, which is available in a printable nomogram which can be distributed to patients and completed together during the clinic visit.

Imaging Analysis

Imaging analysis may also assist with risk stratification. Clinical assessment modules have been developed that incorporate repeat imaging studies for assessment of recannulization of affected veins. In patients with residual vein thrombosis (RVT) at the time anticoagulation was stopped, the hazard ratio for recurrence was 2.4 compared to those without RVT [91].There are a number of ways RVT could impact recurrence, including inpaired venous flow leading to stasis and activation of the coagulation cascade. Subsequent studies used serial ultrasound to determine when to stop anticoagulation. In one study, patients were anticoagulated for 3 months and for those that had RVT, anticoagulation was continued for up to 9 months for provoked and 21 months for unprovoked VTE. In comparison to fixed dosing of 6 months of anti-coagulation, those who had their length of anticoagulation tailored to ultrasonography findings had a lower rate of recurrent VTE [92].Limitations to using RVT in clinical decision-making include lack of a standard definition of RVT and variability in both timing of ultrasound (operator variability) and interpretation of results [93].

Other Options

Another option in patients who are being considered for indefinite anticoagulation is to decrease the intensity of anticoagulation. Since this would theoretically lower the risk of bleeding, the perceived benefit of long-term, low-intensity anticoagulation would be reduction in both bleeding and clotting risk. The PREVENT trial randomized patients who had received full-dose anticoagulation for a median of 6.5 months to either low-intensity warfarin (INR goal of 1.5-2.0 instead of 2.0-3.0) or placebo. In the anticoagulation group, there was a 64% risk reduction in recurrent VTE (hazard ratio 0.36, 95% CI 0.19 to 0.67) but an increased risk of bleeding (hazard ratio 1.92, 95% CI 1.26 to 2.93) [94].The ELATE study randomized patients with unprovoked VTE who had completed 3 or more months of full-intensity warfarin therapy (target INR 2.0–3.0) to continue therapy with either low-intensity warfarin (target INR 1.5–2.0) or full-intensity warfarin (target INR 2.0-3.0). Compared to the low-intensity group, the conventional-intensity group had lower rates of recurrent VTE and no increased rates of major bleeding [95].This study, however, has been criticized because of its overall low bleeding rate in both treatment groups.

Aspirin is an option in patients in whom long-term anticoagulation is untenable. The ASPIRE trial demonstrated that in patients with unprovoked VTE who had completed a course of initial anticoagulation, aspirin 100 mg daily reduced the risk of major vascular events compared to placebo with no increase in bleeding [96].However, aspirin was not associated with a significant reduction in risk of VTE alone (only the composite vascular event endpoint). The WARFASA trial, however, demonstrated that aspirin 100 mg daily was associated with a significant reduction in recurrent VTE compared to placebo after 6 to 18 months of anticoagulation without an increase in major bleeding [97].The absolute risk of recurrence was 11% in the placebo group and 5.9% in the aspirin group. More recently, the INSPIRE collaboration analyzed data from both trials and found that aspirin after initial anticoagulation reduced the risk of recurrent VTE by approximately 42% with a low rate of major bleeding [98].The absolute risk reduction was even larger in men and older patients. For this reason, we recommend aspirin to those patients in whom indefinite anticoagulation may be warranted from the standpoint of reducing risk of recurrent VTE but in whom the risk of bleeding precludes its use.

Hypercoagulable States In Specific Populations

Inherited Thrombophilias

Patients with a hereditary thrombophilia are at increased risk for incident VTE [99].These inherited mutations result in either a loss of normal anticoagulant function or gain of a prothrombotic state. Hereditary disorders associated with VTE include deficiency of antithrombin, protein C, or protein S, or the presence of factor V Leiden and/or prothrombin G20210A mutations. Although deficiency of protein C, protein S, or antithrombin is uncommon and affects only 0.5% of the population, these states have been associated with a 10-fold increased risk of thrombosis in comparison to the general population. Factor V Leiden and prothrombin gene mutation are less likely to be associated with incident thrombosis (2 to 5-fold increased risk of VTE) and are more prevalent in the Caucasian population [100].Though these hereditary thrombophilias increase risk of VTE, prophylactic anti-coagulation prior to a first VTE is not generally indicated.

Data regarding the impact of the inherited thrombophilias on risk of recurrent VTE is less well defined. While some data suggest that inherited thrombophilias are associated with increased risk of recurrent VTE, the degree of impact may be clinically modest especially in those with heterozygous factor V Leiden or prothrombin gene mutations [101].Ideally, a clinical trial would be designed to assess whether hereditary thrombophilia testing is beneficial for patients with VTE in decision-making regarding length of anticoagulation, type of anticoagulation, and risk of recurrence. If a patient with a low-risk inherited thrombophilia has a DVT in the setting of an additional provoking risk factor (surgery, pregnancy, etc), a 3-month course of anticoagulation followed by D-dimer assessment as above is reasonable. If a patient with an inherited thrombophilia experiences an idiopathic VTE, or if a patient with a “high-risk” thrombophilia as described above experiences any type of VTE, we generally recommend indefinite anticoagulation in the absence of high bleeding risk, though again this is a very patient-dependent choice.

Acquired Thrombophilias

Antiphospholipid Syndrome

Antibodies directed against proteins that bind phospho-lipids are associated with an acquired hypercoagulable state. The autoantibodies are categorized as antiphospho-lipid antibodies (APLAs), which include anticardiolipin antibodies (IgG and IgM), beta-2 glycoprotein 1 antibodies (anti-B2 GP), and lupus anticoagulant. These antibodies can form autonomously, as seen in primary disorders, or in association with autoimmune disease as a secondary disorder.

Criteria have been developed to distinguish antiphospholipid-associated clotting disorders from other forms of thrombophilia. The updated Sapporo criteria depend on both laboratory and clinical diagnostic criteria [102].The laboratory diagnosis of APLAs requires the presence of lupus anticoagulants, anticardiolipin antibodies, or anti-B2 GP on at least 2 assays at least 12 weeks apart with elevation above the 99th percentile of the testing laboratory’s normal distribution [103].Testing for lupus anticoagulant is based on 3 stages, the first of which is inhibition of phospholipid-dependent coagulation tests with prolonged clotting time (eg, aPTT or dilute Russell’s viper venom time). The diagnosis is confirmed by a secondary test in which excess hexagonal phase phospholipids are added to incubate with the patient’s plasma to absorb the APLA [104].The presence of anticardiolipin antibodies and anti beta-2 GP antibodies is determined using ELISA based immunoassays. Unlike most other thrombophilias, antiphospholipid syndrome is associated with both arterial and venous thromboembolic events and may be an indication for lifelong anticoagulation after a first thrombotic event. We generally recommend indefinite anticoagulation in the absence of significant bleeding risk.

Cancer-Associated Hypercoagulable State

Patients with cancer have a propensity for thromboembolic events. The underlying mechanisms responsible for cancer-associated clotting events are multifactorial and an area of intense research. Tumor cells can initiate activation of the clotting cascade through release of tissue factor and other pro-coagulant molecules [105].Type and stage of cancer impact risk of VTE, and the tumor itself can compress vasculature leading to venous stasis. Furthermore, chemotherapy, hormone therapy, antiangiogenic drugs, erythropoietin agents, and indwelling central venous catheters all are associated with increased risk of thrombotic events. Approximately 25% of all cancer patients will experience a thrombotic event during the course of their disease [106]. In fact, the presence of a spontaneous clot may be a harbinger of underlying malignancy [107].Approximately 10% of patients who present with an idiopathic VTE are diagnosed with cancer in the next 1 to 2 years.

The utility of extensive cancer screening in patients with spontaneous clotting events is often debated. The small studies that have addressed cancer associated clots have not demonstrated any mortality benefit with extensive screening. A prospective cohort study addressed the utility of limited versus extensive screening [108].In this study, all patients underwent a series of basic screening tests such as history taking, physical examination, chest radiograph, and basic laboratory parameters. Approximately half of the patients underwent additional testing (CT of chest and abdomen and mammography for women). Screening did not result in increased survival or fewer cancer-related deaths. 3.5 % of patients in the extensive screening group were diagnosed with malignancy in comparison to 2.4% in the limited screening group. During follow-up, cancer was diagnosed in 3.7% and 5.0% in the extensive and limited screening groups, respectively. The authors concluded that the low yield of extensive screening and lack of survival benefit did not warrant routinely ordering cancer screening tests above and beyond age-appropriate screening in patients with idiopathic VTE. However, it is known that identification of occult malignancy at an earlier stage of disease is beneficial, and cancer diagnosed within one year of an episode of VTE is generally more advanced and associated with a poorer prognosis [109].It is our practice to take a through history from patients with unprovoked clots particularly focusing on symptoms suggestive of an underlying cancer. We recommend that patients be up to date with all age-appropriate cancer screening.

Heparin-based products (rather than warfarin) are recommended for long-term treatment of cancer-associated DVT. Several trials, most prominently the CLOT trial, have demonstrated that LMWH is associated with reduced risk of recurrent VTE compared with warfarin in cancer patients [110].Fondaparinux may be a reasonable alternative if a patient is unable to tolerate a LMWH. In terms of treatment duration, patients with cancer-associated VTE should be anticoagulated indefinitely as long as they continue to have evidence of active malignancy and/or remain on antineoplastic treatment [111].

Heparin has potential anticancer effects beyond its anticoagulation properties. It is believed that heparin use in patients with cancer can influence cancer progression by acting as an antimetastatic agent. The molecular mechanisms underlying this significant observation are not completely understood, although the first documented benefit of these drugs dates back to the 1970s [112].Overall, LMWH have been associated with improved overall survival in cancer patients and this effect appears to be distinct from its ability to prevent life-threatening VTE episodes [113].

Estrogen-Related Thromboembolic Disease

Pregnancy is a well-established acquired hypercoagulable state, and thromboembolic disease accounts for significant morbidity and mortality in pregnancy and the postpartum period. Approximately 1 in 1000 women will suffer from a thrombotic event during pregnancy or shortly after delivery [8]. The etiology of the tendency to clot during pregnancy is multifactorial and mainly reflects venous stasis due to vasculature compression by the uterus, changes in coagulation factors as the pregnancy progresses, and endothelial damage during delivery, especially Cesarean section. Both factor VIII and von Willebrand factor levels increase, especially in the final months of pregnancy. Simultaneously, levels of the natural anticoagulant protein S diminish, leading to an acquired resistance to activated protein C which results in increased thrombin generation and therefore a hypercoagulable state [114].The risk of thrombosis in pregnancy is clearly heightened in women with inherited thrombophilias, especially in the postpartum period [115].

Similarly to pregnancy, hormone-based contraceptive agents and estrogen replacement therapies are also associated with increased thrombotic risk. Over the years, drug manufacturers have tried to mitigate the clotting risk associated with these drugs by reducing the amount of estrogen and altering the type of progesterone used, yet a risk still remains, resulting in a VTE incidence 2 to 7 times higher in this population [116].The risk is highest in the first 4 months of use and is unaffected by duration of use; risk extends for 3 months after cessation of estrogen-containing therapy. Patients who develop VTE while taking an oral contraceptive are generally instructed to stop the contraceptive and consider an alternative form of birth control. Although routine screening for thrombophilia is not offered to women before prescribing oral contraceptives, a thorough personal and family history regarding venous and arterial thrombotic events as well as recurrent pregnancy loss in women should be taken to evaluate thromboembolic risk factors. We generally avoid use of oral contraceptives in patients with a known hereditary thrombophilia, and consider screening prior to initiation of therapy in those with a strong family history of VTE.

Superficial VTE

Although the main disorders that comprise VTE are DVT and PE, another common presentation is superficial venous thromboembolism (SVT). The risk factors for developing an SVT are similar to those for DVT. In addition, varicose veins also increase the incidence of developing SVT [117].SVT is not associated with excessive mortality, and the main concern with it is progression to DVT. About 25% of patients diagnosed with SVT may have DVT or PE at the time of diagnosis and about 3% without DVT or PE at time of diagnosis developed one of these complications over the following 3 months; clot propagation is another common complication [118].Ultrasound may be of utility in diagnosing occult DVT in patients who initially diagnosed with SVT [119].

For patients who have only SVT at baseline without concomitant DVT or PE, it is difficult to determine which patients are at risk for developing DVT. Some risk stratification models include clot location. Since SVT clots usually develop in the saphenous vein, the clot would need to either progress from the sapheno-femoral junction to the common femoral vein; thus, any clots located near the sapheno-femoral junction are at risk of progressing into the deep vasculature [120].Clots within 3 cm of the junction may be more likely to progress to DVT [121].Chengelis and colleagues feel that proximal saphenous vein thrombosis should likely be treated with anticoagulation [122].Others have taken a more general approach, stating that all clots above the knee or in the thigh area should be treated aggressively [123].

There are solid data for the use of anticoagulation in SVT. In the STEFLUX (Superficial ThromboEmbolism and Fluxum) study, participants received the LMWH parnaparin at one of 3 doses: 8500 IU once daily for 10 days followed by placebo for 20 days, 8500 IU once daily for 10 days and then 6400 IU once daily for 20 days, or 4250 IU once daily for 30 days. Those who received the intermediate dosing had lower rates of DVT, PE, and relapse/SVT recurrence in the first 33 days [124].In the CALISTO trial, fondaparinux 2.5mg per day for 45 days effectively reduced the risk of symptomatic DVT, PE, or SVT recurrence or extension and was not associated with any increased major bleeding compared to placebo [125].A Cochrane review included 30 studies involving over 6500 participants with SVT of the lower extremities. The treatments used in these studies included fondaparinux, LMWH, UFH, non-steriodal anti-inflammatory agents, topical treatment, and surgery. According to the findings, use of fondaparinux at prophylactic dosing for 6 weeks is considered a valid therapeutic option for SVT [126].It is our practice to consider the use of anticoagulants (generally LMWH or fondaparinux) as part of the treatment regimen for SVT.

Target-Specific Oral Anticoagulants And Treatment of VTE

Because of warfarin’s narrow therapeutic window, need for frequent monitoring, significant drug and food interactions, and unfavorable kinetics, the target-specific oral anticoagulants (TSOACs) have been developed with the aim of offering alternatives to warfarin therapy (Figure 3). These drugs have been developed to inhibit either thrombin or factor Xa to disrupt the coagulation cascade. Since these drugs bind directly to coagulation factor, they are associated with rapid onset of action, a wide therapeutic window, fewer drug interactions than warfarin, and predictable dose-response allowing for fixed dosing without lab monitoring.

The direct thrombin inhibitor dabigatran directly binds to thrombin in a concentration-dependent manner [127].Peak plasma concentration is achieved within 0.5 to 2.0 hours after ingestion, and its half-life is 12 to 17 hours. Use of dabigatran in both primary and secondary prevention of VTE has been extensively studied, especially in orthopedic surgery where there have been 4 main trials (RE-MOBILIZE, RE-MODEL, RE-NOVATE, and RE-NOVATE I and II). While RE-MOBILIZE showed that dabigatran 220 mg or 150 mg once daily was inferior to enoxaparin 30 mg twice daily in preventing VTE after total knee arthroplasty, RE-MODEL and RE-NOVATE I and II demonstrated that dabigatran 150 mg or 220 mg once daily was noninferior to enoxaparin 40 mg once daily for prevention of VTE in patients undergoing total knee replacement and hip replacement [128–131].The side effect profile was also promising, with no significant differences in the frequency of major bleeding between dabigatran and enoxaparin. Pooled data and meta-analyses from these trials have demonstrated that for prevention of VTE associated with hip or knee surgery, dabigatran 220 mg or 150 mg once daily is as effective as 40 mg of enoxaparin given daily or 30 mg given twice a day, with a similar bleeding profile [132,133].

More recently, dabigatran been used in the acute treatment and secondary prevention of VTE. In the RE-COVER trial, dabigatran 150 mg twice daily was compared to warfarin (INR 2–3) in the treatment of acute VTE for 6 months, after an initial treatment period of up to 9 days with LMWH or UFH. Dabigatran was noninferior to warfarin with respect to 6-month incidence of recurrent symptomatic objectively confirmed VTE and related deaths, and was not associated with increased bleeding [134].In the RE-MEDY and RE-SONATE trials of extended anticoagulation, dabigatran was as effective as warfarin for prevention of recurrent VTE when continued after 3 months of initial anticoagulation and associated with less bleeding, and was more effective than placebo in preventing recurrent VTE but associated with a higher risk of bleeding [135].Unexpectedly, the risk of acute coronary syndrome was slightly higher in the dabigatran group than the warfarin group, as seen in other studies.

Rivaroxaban, a TSOAC that targets factor Xa, has also shown efficacy in preventing VTE after knee or hip surgery. The RE-CORD 1-4 studies all focused on the use of rivaroxaban in comparison to enoxaparin and found that rivaroxaban 10 mg once daily was superior to enoxaparin 40 mg once daily in prevention of VTE in total knee and total hip arthroplasty [136–138].Meta-analysis of multiple rivaroxaban VTE prophylaxis trials also demonstrated that rivaroxaban significantly lowered the risk of VTE in these surgical patients in comparison to the use of enoxaparin [139].Prophylactic use of rivaroxaban was also studied in acutely ill hospitalized patients in the MAGELLAN trial. Rivaroxaban 10 mg daily for 35 days was compared to enoxaparin 40 mg daily for 10 days followed by placebo and was found to be noninferior to enoxaparin in reduction of VTE risk at day 10 and superior to placebo at day 35 [140].However, the rate of bleeding, although low in both arms, was higher in the rivaroxaban arm.

Rivaroxaban has been studied in randomized clinical trials for acute treatment of DVT and PE and for extended prophylaxis for recurrent VTE (EINSTEIN-DVT, EINSTEIN-PE and EINSTEIN-Extension, respectively).  The treatment strategy for use of rivaroxaban differed from that of dabigatran (in the RE-COVER trial), as rivaroxaban was used upfront as initial anticoagulation rather than after an initial period of parenteral therapy with LMWH or UFH. In both the DVT and PE trials, rivaroxaban was noninferior to standard treatment with enoxaparin followed by warfarin therapy, with no significant difference in major bleeding at 6 months of treatment [141,142].The extension trial also demonstrated that use of rivaroxaban in comparison to placebo for an additional 6 or 12 months after standard therapy was associated with significantly fewer recurrent VTE [141]. These studies led to FDA approval for rivaroxaban for primary prevention of VTE in patients undergoing elective total hip or knee repair surgery, for treatment of acute DVT or PE, and for extended prophylaxis in patients following initial treatment.

The anti-factor Xa TSOAC apixaban has been studied in similar fashion as rivaroxaban. In the AMPLIFY study, apixaban was given at a dose of 10 mg twice daily for 7 days followed by 5 mg twice daily for 6 months (as monotherapy, without initial parenteral agent) and compared to enoxaparin followed by warfarin for treatment of acute VTE. Apixaban was as effective as warfarin in terms of recurrent symptomatic VTE or VTE-related death, and was associated with significantly fewer bleeding events [143].Extended-duration apixaban given at treatment dose (5 mg twice daily) or at prophylactic dose (2.5 mg twice daily) for 12 months after completion of treatment-dose apixaban for VTE demonstrated superiority to placebo for extended prophylaxis in AMPLIFY-EXT, and there was no increase in major bleeding compared to placebo [144].Apixaban was recently approved by the FDA for both treatment and secondary prophylaxis of VTE.

More recently, a third anti-factor Xa TSOAC edoxaban demonstrated noninferiority to warfarin in prevention of recurrent symptomatic VTE when administered to patients with DVT or PE at 60 mg once daily for 3 to 12 months [145].Edoxaban also led to significantly less bleeding than warfarin. Edoxaban was recently approved by the FDA for treatment of VTE.

These TSOACs show promise in treatment and prevention of VTE but should be used in patients who meet appropriate criteria for renal function, age, and bleeding risk, as there are currently no available antidotes to reverse their effects. If significant bleeding occurs and cannot be controlled by usual maneuvers such as mechanical compression or surgical intervention, there is little data to guide the use of pharmacologic interventions. Plasma dabigatran levels can be reduced through the use of hemodialysis [146].Antibodies capable of neutralizing dabigatran have been developed, and one specific antibody, idarucizumab, was well-tolerated and showed immediate and complete reversal of dabigatran in subjects of different age and renal function [147,148].Andexanet, a modified recombinant derivative of factor Xa with no catalytic activty, acts as a “decoy receptor” with higher affinity to factor Xa inhibitors than natural factor Xa.  Phase II studies in healthy volunteers demonstrated that andexanet immediately reversed the anticoagulation activity of apixaban, rivaroxaban, enoxaparin, and most recently edoxaban without thrombotic consequences [149].Two randomized, double-blind, placebo-controlled phase III studies (ANNEXA-A, looking at the reversal of apixaban, and ANNEXA-R, looking at reversal of rivaroxaban) are underway, and preliminary results show that a single intravenous bolus of andexanet demonstrated almost complete reversal [150].Finally, aripazine (PER977), a synthetic small molecule that binds to heparins as well as all TSOACs, was shown in a phase II trial to decrease blood clotting time to within 10% above baseline value in 10 minutes or less with an effect lasting for 24 hours [151].

Some have advocated for use of prothrombin complex concentrate (PCC) or recombinant factor VIIa for reversal of TSOAC-associated bleeding. Rivaroxaban was demonstrated to be partially reversible by PCC, whereas this approach was not as successful for dabigatran in healthy volunteers [152].In vitro evidence, however, showed that PCC did not significantly change aPTT [153].At present, the use of nonspecific hemostatic agents (including recombinant factor VIIa, 4-factor prothrombin complex concentrate, and activated prothrombin complex concentrates) is suggested for reversal of TSOACs in patients who present with life-threatening bleeding [154,155].

Conclusion

Patients with VTE present with a wide range of findings and factors that impact management. Decision making in VTE management is a fluid process that should be re-evaluated as new data emerge and individual circumstances change. There is more focus on VTE prevention and treatment today than there was even a decade ago. Diagnostic algorithms, identification of new risk factors, refinement in understanding of the pathogenesis of thrombosis, and identification of new anticoagulants with more favorable risk-benefit profiles will all ultimately contribute to improved patient care.

 

Corresponding author: Jean M. Connors, MD, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02215.

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112. Elias EG, Sepulveda F, Mink IB. Increasing the efficiency of cancer chemotherapy with heparin: “clinical study.” J Surg Oncol 1973;5:189–93.

113. Lazo-Langner A, Goss GD, Spaans JN, Rodger MA. The effect of low-molecular-weight heparin on cancer survival. A systematic review and meta-analysis of randomized trials. J Thromb Haemost 2007;5:729–37.

114. Bremme KA. Haemostatic changes in pregnancy. Best Pract Res Clin Haematol 2003;16:153–68.

115. Gerhardt A, Scharf RE, Beckmann MW, et al. Prothrombin and factor V mutations in women with a history of thrombosis during pregnancy and the puerperium. N Engl J Med 2000;342:374–80.

116. Rott H. Thrombotic risks of oral contraceptives. Curr Opin Obstet Gynecol 2012;24:235-40.

117. Marchiori A, Mosena L, Prandoni P. Superficial vein thrombosis: risk factors, diagnosis, and treatment. Semin Thromb Hemost 2006;32:737–43.

118. Decousus H, Quere I, Presles E, et al. Superficial venous thrombosis and venous thromboembolism: a large, prospective epidemiologic study. Ann Intern Med 2010;152:218–24.

119. Galanaud JP, Genty C, Sevestre MA, et al. Predictive factors for concurrent deep-vein thrombosis and symptomatic venous thromboembolic recurrence in case of superficial venous thrombosis. The OPTIMEV study. Thromb Haemost 2011;105:31–9.

120. Sullivan V, Denk PM, Sonnad SS, et al. Ligation versus anticoagulation: treatment of above-knee superficial thrombophlebitis not involving the deep venous system. J Am Coll Surg 2001;193:556–62.

121. Lohr JM, McDevitt DT, Lutter KS, et al. Operative management of greater saphenous thrombophlebitis involving the saphenofemoral junction. Am J Surg 1992;164:269–75.

122. Chengelis DL, Benedick PJ, Glover JL, et al. Progression of superficial venous thrombosis to deep vein thrombosis. J Vasc Surg 1996;24:745–9.

123. Verlato F, Zuccetta P, Prandoni P, et al. An unexpectedly high rate of pulmonary embolism in patients with superficial thrombophlebitis of the leg. J Vasc Surg 1999;30:1113–5.

124. Cosmi B, Filippini M, Tonti D, et al. A randomized double-blind study of low-molecular-weight heparin (parnaparin) for superficial vein thrombosis: STEFLUX (Superficial ThromboEmbolism and Fluxum). J Thromb Haemost 2012;10:1026–35.

125. Decousus H, Prandoni P, Mismetti P, et al. Fondaparinux for the treatment of superficial-vein thrombosis in the legs. N Engl J Med 2010;363:1222–32.

126. Di Nisio M, Wichers IM, Middeldorp S. Treatment for superficial thrombophlebitis of the leg. Cochrane Database Syst Rev 2013;4:CD004982.

127. Stangier J, Rathgen K, Stahle H, et al. The pharmacokinetics, pharmacodynamics and tolerability of dabigatran etexilate, a new oral direct thrombin inhibitor, in healthy male subjects. Br J Clin Pharmacol 2007;64:292–303.

128. RE-MOBILIZE Writing Committee, Ginsberg JS, Davidson BL, et al. Oral thrombin inhibitor dabigatran etexilate vs North American enoxaparin regimen for prevention of venous thromboembolism after knee arthroplasty surgery. J Arthroplasty 2009;24:1–9.

129. Eriksson BI, Dahl OE, Rosencher N, et al. Oral dabigatran etexilate vs. subcutaneous enoxaparin for the prevention of venous thromboembolism after total knee replacement: the RE-MODEL randomized trial. J Thromb Haemost 2007;5:2178–85.

130. Eriksson BI, Dahl OE, Rosencher N, et al. Dabigatran etexilate versus enoxaparin for prevention of venous thromboembolism after total hip replacement: a randomized, double-blind, non-inferiority trial. Lancet 2007;370:949–56.

131. Eriksson BI, Dahl OE, Huo MH, et al. Oral dabigatran versus enoxaparin for thromboprophylaxis after primary total hip arthroplasty (RE-NOVATE II*). A randomized, double-blind, non-inferiority trial. Thromb Haemost2011;105:721–9.

132. Friedman RJ, Dahl OE, Rosencher N, et al. Dabigatran versus enoxaparin for prevention of venous thromboembolism after hip or knee arthroplasty: a pooled analysis of three trials. Thromb Res 2010;126:175–82.

133. Wolowacz SE, Roskell NS, Plumb JM, et al. Efficacy and safety of dabigatran etexilate for the prevention of venous thromboembolism following total hip or knee arthroplasty. A meta-analysis. Thromb Haemost 2009;101:77–85.

134. Schulman S, Kearon C, Kakkar AK, et al. Dabigatran versus warfarin in the treatment of acute venous thromboembolism. N Engl J Med 2009;36:2342–52.

135. Schulman S, Kearon C, Kakkar AK, et al. Extended use of dabigatran, warfarin, or placebo in venous thromboembolism. N Engl J Med 2013;368:709–18.

136. Eriksson BI, Borris LC, Friedman RJ, et al. Rivaroxaban versus enoxaparin for thromboprophylaxis after hip arthroplasty. N Engl J Med 2008;358:2765–75.

137. Kakkar AK, Brenner B, Dahl OE, et al. Extended duration rivaroxaban versus short-term enoxaparin for the prevention of venous thromboembolism after total hip arthroplasty: a double-blind, randomized controlled trial. Lancet 2008;372:31–9.

138. Lassen MR, Ageno W, Borris LC, et al. Rivaroxaban versus enoxaparin for thromboprophylaxis after total knee arthroplasty. N Engl J Med 2008;358:2776–86.

139. Cao YB, Zhang JD, Shen H, Jiang YY. Rivaroxaban versus enoxaparin for thromboprophylaxis after total hip or knee arthroplasty: a meta-analysis of randomized controlled trials. Eur J Clin Pharmacol 2010;66:1099–108.

140. Cohen AT, Spiro TE, Buller HR, et al. Rivaroxaban for thromboprophylaxis in acutely ill medical patients. N Engl J Med 2013;368:513–23.

141. EINSTEIN Investigators, Bauersachs R, Berkowitz SD, et al. Oral rivaroxaban for symptomatic venous thromboembolism. N Engl J Med 2010;363:2499–510.

142. EINSTEIN-PE Investigators, Buller HR, Prins MH, et al. Oral rivaroxaban for the treatment of symptomatic pulmonary embolism. N Engl J Med 2012;366:1287–97.

143. Agnelli G, Buller HR, Cohen A, et al. Oral apixaban for the treatment of acute venous thromboembolism. N Engl J Med 2013;369:799–808.

144. Agnelli G, Buller HR, Cohen A, et al. Apixaban for extended treatment of venous thromboembolism. N Engl J Med 2013;368:699–708.

145. Hokusai-VTE investigators, Buller HR, Decousus H, et al. Edoxaban versus warfarin for the treatment of symptomatic venous thromboembolism. N Engl J Med 2013;369:1406–15.

146. Khadzhynov D, Wagner F, Formella S, et al. Effective elimination of dabigatran by haemodialysis. A phase I single-centre study in patients with end-stage renal disease. Thromb Haemost 2013;109:596–605.

147. Schiele F, van Ryn J, Canada K, et al. A specific antidote for dabigatran: functional and structural characterization. Blood 2013;121:3554–62.

148. Glund S, Stangier J, Schmohl M, et al. Idarucizumab, a specific antidote for dabigatran: immediate, complete, and sustained reversal of dabigatran induced anticoagulation in elderly and renally impaired subjects. Presented at the American Society of Hematology 2014 Annual Meeting; San Francisco, CA.

149. Crowther MA, Levy GG, Lu G, et al. A phase 2 randomized, double-blind, placebo-controlled trial demonstrating reversal of edoxaban-induced anticoagulation in healthy subjects by andexanet alfa (PRT064445), a universal antidote for factor Xa (fXa) inhibitors. Abstract #4269. Presented at the American Society of Hematology 2014 Annual Meeting, San Francisco, CA.

150. Crowther M, Levy GG, Lu G, et al. ANNEXATM-A: A phase 3 randomized, double-blind, placebo-controlled trial, demonstrating reversal of apixaban-induced anticoagulation in older subjects by andexanet alfa (PRT 064445), a universal antidote for factor Xa (fa) inhibitors. Presented at the American Heart Association 2014 Annual Meeting, Chicago, IL.

151. Ansell JE, Bakhru SH, Lauliche BE, et al. Use of PER977 to reverse the anticoagulant effect of edoxaban. N Engl J Med 2014;371:2141–2.

152. Eerenberg ES, Kamphuisen PW, Sijpkens MK, et al. Reversal of rivaroxaban and dabigatran by prothrombin complex concentrate: a randomized, placebo-controlled, crossover study in healthy subjects. Circulation 2011;124:1573–9.

153. Korber MK, Langer E, Ziemer S, et al. Measurement and reversal of prophylactic and therapeutic peak levels of rivaroxaban: an in vitro study. Clin Appl Thromb Hemost 2014;20:735–40.

154. Fawole A, Daw HA, Crowther MA. Practical management of bleeding due to the anticoagulants dabigatran, rivaroxaban, and apixaban. Cleve Clin J Med 2013;80:443–51.

155. Siegal DM, Garcia DA, Crowther MA. How I treat target-specific oral anticoagulant-associated bleeding. Blood 2014;123:1152–8.

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When Should Hypopituitarism Be Suspected?

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When Should Hypopituitarism Be Suspected?

(click for larger image)Pituitary gland in the brain. Computer artwork of a person's head showing the left hemisphere of the brain inside. The highlighted area (center) shows the pituitary gland. The pituitary gland is a small endocrine gland about the size of a pea protruding off the bottom of the hypothalamus at the base of the brain. It secretes hormones regulating homoeostasis, including trophic hormones that stimulate other endocrine glands. It is functionally connected to and influenced by the hypothalamus.Image Credit: Roger Harris / Science Source

Case

A 53-year-old woman with a history of a suprasellar meningioma resected nine years ago with recurrence of a 4.5x2 cm mass one year ago and recent ventriculoperitoneal (VP) shunt placement for hydrocephalus presented with altered mental status (AMS) and hallucinations. She was admitted for radiation therapy to the mass. The patient had little improvement in her mental status four weeks into a six-week, 4860 cGy course of photon therapy.

The internal medicine service was consulted for new onset tachycardia (103), hypotension (83/55), and fever (38.6 C). Laboratory data revealed a white blood cell count 4.8 x 109 cells/L, sodium 137 mmol/L, potassium 4.1 mmol/L, chloride 110 mmol/L, bicarbonate 28 mmol/L, blood urea nitrogen 3 mg/dl, creatinine 0.6 mg/dl, and glucose 91 mg/dl. Thyroid-stimulating hormone (TSH) was low at 0.38 mIU/mL. Urine specific gravity was 1.006. Workups for infectious and thromboembolic diseases were unremarkable.

Discussion

Hypopituitarism is a disorder of impaired hormone production from the anterior and, less commonly, posterior pituitary gland. The condition can originate from several broad categories of diseases affecting the hypothalamus, pituitary stalk, or pituitary gland. In adults, the etiology is often from the mass effect of tumors or from treatment with surgery or radiotherapy. Other causes include vascular, infectious, infiltrative, inflammatory, and idiopathic. Well-substantiated data on the incidence and prevalence of hypopituitarism is sparse. It has an estimated prevalence of 45.5 cases per 100,000 and incidence of 4.2 cases per 100,000 per year.1

Clinical manifestations of hypopituitarism depend on the type and severity of hormone deficiency. The consequences of adrenal insufficiency (AI) range from smoldering and nonspecific findings (e.g. fatigue, lethargy, indistinct gastrointestinal symptoms, eosinophilia, fever) to full-fledged crisis (e.g. AMS, severe electrolyte abnormalities, hemodynamic compromise, shock). The presentation of central AI (i.e., arising from hypothalamic or pituitary pathology) is often more subtle than primary AI. In central AI, only glucocorticoid (GC) function is disrupted, leaving the renin-angiotensin-aldosterone system and mineralocorticoid (MC) function intact. This is in stark contrast to primary AI resulting from direct adrenal gland injury, which nearly always disrupts both GC and MC function, leading to more profound circulatory collapse and electrolyte disturbance.2

Aside from orthostatic blood pressure or possible low-grade fever, few physical exam features are associated with central AI. Hyperpigmentation is not seen due to the lack of anterior pituitary-derived melanocortins that stimulate melanocytes and induce pigmentation. As for laboratory findings, hyperkalemia is a feature of primary AI (due to hypoaldosteronism) but is not seen in central AI. Hyponatremia occurs in both types of AI and is vasopressin-mediated. Hyponatremia is more common in primary AI, resulting from appropriate vasopressin release that occurs due to hypotension. Hyponatremia also occurs in secondary AI because of increased vasopressin secretion mediated directly by hypocortisolemia.3,4

In summary, hyperpigmentation and the electrolyte pattern of hyponatremia and hyperkalemia are distinguishing clinical characteristics of primary AI, occurring in up to 90% of cases, but these features would not be expected with central AI.5

In the hospitalized patient with multiple active acute illnesses and infectious risk factors, it can be difficult to recognize the diagnosis of AI or hypopituitarism. Not only do signs and symptoms frequently overlap, but concomitant acute illness is usually a triggering event. Crisis should be suspected in the setting of unexplained fever, dehydration, or shock out of proportion to severity of current illness.5

 

 

Not surprisingly, high rates of partial or complete hypopituitarism are seen in patients following surgical removal of pituitary tumors or nearby neoplasms (e.g. craniopharyngiomas). Both surgery and radiotherapy for non-pituitary brain tumors are also major risk factors for development of hypopituitarism, occurring in up to 38% and 41% of patients, respectively.6 The strongest predictors of hormone failure are higher radiation doses, proximity to the pituitary-hypothalamus, and longer time interval after completion of radiotherapy. Within 10 years after a median dose of 5000 rad (50Gy) directed at the skull base, nasopharynx, or cranium, up to three-fourths of patients will develop some degree of pituitary insufficiency. Later onset of hormone failure usually reflects hypothalamic injury, whereas higher irradiation doses can lead to earlier onset pituitary damage.5

Not all hormone-secreting cells of the hypothalamus or pituitary are equally susceptible to injury; there is a characteristic sequence of hormonal failure. The typical order of hormone deficiency from pituitary compression or destruction is as follows: growth hormone (GH) > follicle-stimulating hormone (FSH) > luteinizing hormone (LH) > TSH > adrenocorticotropic hormone (ACTH) > vasopressin. A similar pattern is seen following brain irradiation: GH > FSH and LH > ACTH > TSH. A recent systematic review of 18 studies with 813 patients receiving cranial radiotherapy for non-pituitary tumors found pituitary dysfunction was 45% for GH deficiency, compared to 22% for ACTH deficiency.7

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.

Biochemical diagnosis of hypopituitarism consists of measuring the various pituitary and target hormone levels as well as provocation testing. When interpreting these tests, whether to identify excess or deficient states, it is important to remember the individual values are part of the broader hypothalamic-pituitary axis feedback loops. Thus, it can be more useful designating if a high or low test value is appropriately or inappropriately high or low. In the presented case, low TSH level could be misinterpreted as excess thyroid hormone supplementation. An appropriately elevated free T4 level would confirm this, but an inappropriately low free T4 would raise suspicion of central hypothalamic-pituitary dysfunction.

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.2 Rapid administration with intravenous levothyroxine can be given in severe hypothyroidism or myxedema.

“Stress-dose” steroids are generally recommended for patients who are also administered levothyroxine, as the desired increased in metabolic rate can deplete existing pituitary-adrenocortical hormone reserves, precipitating adrenal crisis.5 Stress-dose corticosteroids also ensure recruitment of a mineralocorticoid response. Cortisol has both GC and MC stimulating effects but is rapidly metabolized to cortisone, which lacks MC stimulating effects. Thus, high doses overwhelm this conversion step and allow remaining cortisol to stimulate MC receptors.2 These high doses may not be necessary in secondary AI (i.e., preserved MC function) but would be reasonable in an unstable patient or until confirmation is made with an inappropriately low ACTH.

Back to the Case

Morning cortisol returned undetectable, and ACTH was 14 pg/mL (6-58). Past records revealed a down-trending TSH from 1.12 to 0.38 mIU/mL, which had inappropriately prompted a levothyroxine dose reduction from 50 mcg to 25 mcg. A free thyroxine (T4) was low at 0.67 ng/dL (0.89-1.76). Estradiol, FSH, and LH were undetectable. Prolactin was 23 ng/mL (3-27). She was started on prednisone, 5 mg daily, and her levothyroxine was adjusted to a weight-based dose. Her fever resolved with the initiation of prednisone, and all cultures remained negative. Over two weeks, she improved back to her baseline, was discharged to a rehabilitation center, and eventually returned home.

 

 


Dr. Inman is a hospitalist at St. Mary’s Hospital and Regional Medical Center in Grand Junction, Colo. Dr. Bridenstine is an endocrinologist at the University of Colorado Denver. Dr. Cumbler is a hospitalist at the University of Colorado Denver.

Key Points

  • Central adrenal insufficiency lacks the hyperpigmentation and hyperkalemia associated with primary adrenal insufficiency.
  • Central adrenal insufficiency should be suspected in cases of tumors or surgery in the region of the pituitary; presentation can be delayed following intracranial radiation therapy.
  • In cases of shock due to suspected panhypopituitarism, intravenous levothyroxine should be accompanied by stress-dose steroids while awaiting laboratory confirmation.
  • When secondary (i.e., central) hormone deficiencies are suspected, check both pituitary and target organ hormones (e.g. TSH and free T4) to determine if the hypothalamic-pituitary-target organ axis is “appropriate.” Provocation testing may be necessary to confirm.

References

  1. Regal M, Pàramo C, Sierra SM, Garcia-Mayor RV. Prevalence and incidence of hypopituitarism in an adult Caucasian population in northwestern Spain. Clin Endocrinol. 2001;55(6):735-740.
  2. Bouillon R. Acute adrenal insufficiency. Endocrinol Metab Clin North Am. 2006;35(4):767-75, ix.
  3. Raff H. Glucocorticoid inhibition of neurohypophysial vasopressin secretion. Am J Physiol. 1987;252(4 Pt 2):R635-644.
  4. Erkut ZA, Pool C, Swaab DF. Glucocorticoids suppress corticotropin-releasing hormone and vasopressin expression in human hypothalamic neurons. J Clin Endocrinol Metab. 1998;83(6):2066-2073.
  5. Melmed S, Polonski KS, Reed Larsen P, Kronenberg HM. Williams Textbook of Endocrinology. 12th ed. Philadelphia, Pa.: Saunders/Elsevier; 2012.
  6. Schneider HJ, Aimaretti G, Kreitschmann-Andermahr I, Stalla GK, Ghigo E. Hypopituitarism. Lancet. 2007;369(9571):1461-1470.
  7. Appelman-Dijkstra NM, Kokshoorn NE, Dekkers OM, et al. Pituitary dysfunction in adult patients after cranial radiotherapy: systematic review and meta-analysis. J Clin Endocrinol Metabol. 2011;96(8):2330-2340.
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(click for larger image)Pituitary gland in the brain. Computer artwork of a person's head showing the left hemisphere of the brain inside. The highlighted area (center) shows the pituitary gland. The pituitary gland is a small endocrine gland about the size of a pea protruding off the bottom of the hypothalamus at the base of the brain. It secretes hormones regulating homoeostasis, including trophic hormones that stimulate other endocrine glands. It is functionally connected to and influenced by the hypothalamus.Image Credit: Roger Harris / Science Source

Case

A 53-year-old woman with a history of a suprasellar meningioma resected nine years ago with recurrence of a 4.5x2 cm mass one year ago and recent ventriculoperitoneal (VP) shunt placement for hydrocephalus presented with altered mental status (AMS) and hallucinations. She was admitted for radiation therapy to the mass. The patient had little improvement in her mental status four weeks into a six-week, 4860 cGy course of photon therapy.

The internal medicine service was consulted for new onset tachycardia (103), hypotension (83/55), and fever (38.6 C). Laboratory data revealed a white blood cell count 4.8 x 109 cells/L, sodium 137 mmol/L, potassium 4.1 mmol/L, chloride 110 mmol/L, bicarbonate 28 mmol/L, blood urea nitrogen 3 mg/dl, creatinine 0.6 mg/dl, and glucose 91 mg/dl. Thyroid-stimulating hormone (TSH) was low at 0.38 mIU/mL. Urine specific gravity was 1.006. Workups for infectious and thromboembolic diseases were unremarkable.

Discussion

Hypopituitarism is a disorder of impaired hormone production from the anterior and, less commonly, posterior pituitary gland. The condition can originate from several broad categories of diseases affecting the hypothalamus, pituitary stalk, or pituitary gland. In adults, the etiology is often from the mass effect of tumors or from treatment with surgery or radiotherapy. Other causes include vascular, infectious, infiltrative, inflammatory, and idiopathic. Well-substantiated data on the incidence and prevalence of hypopituitarism is sparse. It has an estimated prevalence of 45.5 cases per 100,000 and incidence of 4.2 cases per 100,000 per year.1

Clinical manifestations of hypopituitarism depend on the type and severity of hormone deficiency. The consequences of adrenal insufficiency (AI) range from smoldering and nonspecific findings (e.g. fatigue, lethargy, indistinct gastrointestinal symptoms, eosinophilia, fever) to full-fledged crisis (e.g. AMS, severe electrolyte abnormalities, hemodynamic compromise, shock). The presentation of central AI (i.e., arising from hypothalamic or pituitary pathology) is often more subtle than primary AI. In central AI, only glucocorticoid (GC) function is disrupted, leaving the renin-angiotensin-aldosterone system and mineralocorticoid (MC) function intact. This is in stark contrast to primary AI resulting from direct adrenal gland injury, which nearly always disrupts both GC and MC function, leading to more profound circulatory collapse and electrolyte disturbance.2

Aside from orthostatic blood pressure or possible low-grade fever, few physical exam features are associated with central AI. Hyperpigmentation is not seen due to the lack of anterior pituitary-derived melanocortins that stimulate melanocytes and induce pigmentation. As for laboratory findings, hyperkalemia is a feature of primary AI (due to hypoaldosteronism) but is not seen in central AI. Hyponatremia occurs in both types of AI and is vasopressin-mediated. Hyponatremia is more common in primary AI, resulting from appropriate vasopressin release that occurs due to hypotension. Hyponatremia also occurs in secondary AI because of increased vasopressin secretion mediated directly by hypocortisolemia.3,4

In summary, hyperpigmentation and the electrolyte pattern of hyponatremia and hyperkalemia are distinguishing clinical characteristics of primary AI, occurring in up to 90% of cases, but these features would not be expected with central AI.5

In the hospitalized patient with multiple active acute illnesses and infectious risk factors, it can be difficult to recognize the diagnosis of AI or hypopituitarism. Not only do signs and symptoms frequently overlap, but concomitant acute illness is usually a triggering event. Crisis should be suspected in the setting of unexplained fever, dehydration, or shock out of proportion to severity of current illness.5

 

 

Not surprisingly, high rates of partial or complete hypopituitarism are seen in patients following surgical removal of pituitary tumors or nearby neoplasms (e.g. craniopharyngiomas). Both surgery and radiotherapy for non-pituitary brain tumors are also major risk factors for development of hypopituitarism, occurring in up to 38% and 41% of patients, respectively.6 The strongest predictors of hormone failure are higher radiation doses, proximity to the pituitary-hypothalamus, and longer time interval after completion of radiotherapy. Within 10 years after a median dose of 5000 rad (50Gy) directed at the skull base, nasopharynx, or cranium, up to three-fourths of patients will develop some degree of pituitary insufficiency. Later onset of hormone failure usually reflects hypothalamic injury, whereas higher irradiation doses can lead to earlier onset pituitary damage.5

Not all hormone-secreting cells of the hypothalamus or pituitary are equally susceptible to injury; there is a characteristic sequence of hormonal failure. The typical order of hormone deficiency from pituitary compression or destruction is as follows: growth hormone (GH) > follicle-stimulating hormone (FSH) > luteinizing hormone (LH) > TSH > adrenocorticotropic hormone (ACTH) > vasopressin. A similar pattern is seen following brain irradiation: GH > FSH and LH > ACTH > TSH. A recent systematic review of 18 studies with 813 patients receiving cranial radiotherapy for non-pituitary tumors found pituitary dysfunction was 45% for GH deficiency, compared to 22% for ACTH deficiency.7

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.

Biochemical diagnosis of hypopituitarism consists of measuring the various pituitary and target hormone levels as well as provocation testing. When interpreting these tests, whether to identify excess or deficient states, it is important to remember the individual values are part of the broader hypothalamic-pituitary axis feedback loops. Thus, it can be more useful designating if a high or low test value is appropriately or inappropriately high or low. In the presented case, low TSH level could be misinterpreted as excess thyroid hormone supplementation. An appropriately elevated free T4 level would confirm this, but an inappropriately low free T4 would raise suspicion of central hypothalamic-pituitary dysfunction.

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.2 Rapid administration with intravenous levothyroxine can be given in severe hypothyroidism or myxedema.

“Stress-dose” steroids are generally recommended for patients who are also administered levothyroxine, as the desired increased in metabolic rate can deplete existing pituitary-adrenocortical hormone reserves, precipitating adrenal crisis.5 Stress-dose corticosteroids also ensure recruitment of a mineralocorticoid response. Cortisol has both GC and MC stimulating effects but is rapidly metabolized to cortisone, which lacks MC stimulating effects. Thus, high doses overwhelm this conversion step and allow remaining cortisol to stimulate MC receptors.2 These high doses may not be necessary in secondary AI (i.e., preserved MC function) but would be reasonable in an unstable patient or until confirmation is made with an inappropriately low ACTH.

Back to the Case

Morning cortisol returned undetectable, and ACTH was 14 pg/mL (6-58). Past records revealed a down-trending TSH from 1.12 to 0.38 mIU/mL, which had inappropriately prompted a levothyroxine dose reduction from 50 mcg to 25 mcg. A free thyroxine (T4) was low at 0.67 ng/dL (0.89-1.76). Estradiol, FSH, and LH were undetectable. Prolactin was 23 ng/mL (3-27). She was started on prednisone, 5 mg daily, and her levothyroxine was adjusted to a weight-based dose. Her fever resolved with the initiation of prednisone, and all cultures remained negative. Over two weeks, she improved back to her baseline, was discharged to a rehabilitation center, and eventually returned home.

 

 


Dr. Inman is a hospitalist at St. Mary’s Hospital and Regional Medical Center in Grand Junction, Colo. Dr. Bridenstine is an endocrinologist at the University of Colorado Denver. Dr. Cumbler is a hospitalist at the University of Colorado Denver.

Key Points

  • Central adrenal insufficiency lacks the hyperpigmentation and hyperkalemia associated with primary adrenal insufficiency.
  • Central adrenal insufficiency should be suspected in cases of tumors or surgery in the region of the pituitary; presentation can be delayed following intracranial radiation therapy.
  • In cases of shock due to suspected panhypopituitarism, intravenous levothyroxine should be accompanied by stress-dose steroids while awaiting laboratory confirmation.
  • When secondary (i.e., central) hormone deficiencies are suspected, check both pituitary and target organ hormones (e.g. TSH and free T4) to determine if the hypothalamic-pituitary-target organ axis is “appropriate.” Provocation testing may be necessary to confirm.

References

  1. Regal M, Pàramo C, Sierra SM, Garcia-Mayor RV. Prevalence and incidence of hypopituitarism in an adult Caucasian population in northwestern Spain. Clin Endocrinol. 2001;55(6):735-740.
  2. Bouillon R. Acute adrenal insufficiency. Endocrinol Metab Clin North Am. 2006;35(4):767-75, ix.
  3. Raff H. Glucocorticoid inhibition of neurohypophysial vasopressin secretion. Am J Physiol. 1987;252(4 Pt 2):R635-644.
  4. Erkut ZA, Pool C, Swaab DF. Glucocorticoids suppress corticotropin-releasing hormone and vasopressin expression in human hypothalamic neurons. J Clin Endocrinol Metab. 1998;83(6):2066-2073.
  5. Melmed S, Polonski KS, Reed Larsen P, Kronenberg HM. Williams Textbook of Endocrinology. 12th ed. Philadelphia, Pa.: Saunders/Elsevier; 2012.
  6. Schneider HJ, Aimaretti G, Kreitschmann-Andermahr I, Stalla GK, Ghigo E. Hypopituitarism. Lancet. 2007;369(9571):1461-1470.
  7. Appelman-Dijkstra NM, Kokshoorn NE, Dekkers OM, et al. Pituitary dysfunction in adult patients after cranial radiotherapy: systematic review and meta-analysis. J Clin Endocrinol Metabol. 2011;96(8):2330-2340.

(click for larger image)Pituitary gland in the brain. Computer artwork of a person's head showing the left hemisphere of the brain inside. The highlighted area (center) shows the pituitary gland. The pituitary gland is a small endocrine gland about the size of a pea protruding off the bottom of the hypothalamus at the base of the brain. It secretes hormones regulating homoeostasis, including trophic hormones that stimulate other endocrine glands. It is functionally connected to and influenced by the hypothalamus.Image Credit: Roger Harris / Science Source

Case

A 53-year-old woman with a history of a suprasellar meningioma resected nine years ago with recurrence of a 4.5x2 cm mass one year ago and recent ventriculoperitoneal (VP) shunt placement for hydrocephalus presented with altered mental status (AMS) and hallucinations. She was admitted for radiation therapy to the mass. The patient had little improvement in her mental status four weeks into a six-week, 4860 cGy course of photon therapy.

The internal medicine service was consulted for new onset tachycardia (103), hypotension (83/55), and fever (38.6 C). Laboratory data revealed a white blood cell count 4.8 x 109 cells/L, sodium 137 mmol/L, potassium 4.1 mmol/L, chloride 110 mmol/L, bicarbonate 28 mmol/L, blood urea nitrogen 3 mg/dl, creatinine 0.6 mg/dl, and glucose 91 mg/dl. Thyroid-stimulating hormone (TSH) was low at 0.38 mIU/mL. Urine specific gravity was 1.006. Workups for infectious and thromboembolic diseases were unremarkable.

Discussion

Hypopituitarism is a disorder of impaired hormone production from the anterior and, less commonly, posterior pituitary gland. The condition can originate from several broad categories of diseases affecting the hypothalamus, pituitary stalk, or pituitary gland. In adults, the etiology is often from the mass effect of tumors or from treatment with surgery or radiotherapy. Other causes include vascular, infectious, infiltrative, inflammatory, and idiopathic. Well-substantiated data on the incidence and prevalence of hypopituitarism is sparse. It has an estimated prevalence of 45.5 cases per 100,000 and incidence of 4.2 cases per 100,000 per year.1

Clinical manifestations of hypopituitarism depend on the type and severity of hormone deficiency. The consequences of adrenal insufficiency (AI) range from smoldering and nonspecific findings (e.g. fatigue, lethargy, indistinct gastrointestinal symptoms, eosinophilia, fever) to full-fledged crisis (e.g. AMS, severe electrolyte abnormalities, hemodynamic compromise, shock). The presentation of central AI (i.e., arising from hypothalamic or pituitary pathology) is often more subtle than primary AI. In central AI, only glucocorticoid (GC) function is disrupted, leaving the renin-angiotensin-aldosterone system and mineralocorticoid (MC) function intact. This is in stark contrast to primary AI resulting from direct adrenal gland injury, which nearly always disrupts both GC and MC function, leading to more profound circulatory collapse and electrolyte disturbance.2

Aside from orthostatic blood pressure or possible low-grade fever, few physical exam features are associated with central AI. Hyperpigmentation is not seen due to the lack of anterior pituitary-derived melanocortins that stimulate melanocytes and induce pigmentation. As for laboratory findings, hyperkalemia is a feature of primary AI (due to hypoaldosteronism) but is not seen in central AI. Hyponatremia occurs in both types of AI and is vasopressin-mediated. Hyponatremia is more common in primary AI, resulting from appropriate vasopressin release that occurs due to hypotension. Hyponatremia also occurs in secondary AI because of increased vasopressin secretion mediated directly by hypocortisolemia.3,4

In summary, hyperpigmentation and the electrolyte pattern of hyponatremia and hyperkalemia are distinguishing clinical characteristics of primary AI, occurring in up to 90% of cases, but these features would not be expected with central AI.5

In the hospitalized patient with multiple active acute illnesses and infectious risk factors, it can be difficult to recognize the diagnosis of AI or hypopituitarism. Not only do signs and symptoms frequently overlap, but concomitant acute illness is usually a triggering event. Crisis should be suspected in the setting of unexplained fever, dehydration, or shock out of proportion to severity of current illness.5

 

 

Not surprisingly, high rates of partial or complete hypopituitarism are seen in patients following surgical removal of pituitary tumors or nearby neoplasms (e.g. craniopharyngiomas). Both surgery and radiotherapy for non-pituitary brain tumors are also major risk factors for development of hypopituitarism, occurring in up to 38% and 41% of patients, respectively.6 The strongest predictors of hormone failure are higher radiation doses, proximity to the pituitary-hypothalamus, and longer time interval after completion of radiotherapy. Within 10 years after a median dose of 5000 rad (50Gy) directed at the skull base, nasopharynx, or cranium, up to three-fourths of patients will develop some degree of pituitary insufficiency. Later onset of hormone failure usually reflects hypothalamic injury, whereas higher irradiation doses can lead to earlier onset pituitary damage.5

Not all hormone-secreting cells of the hypothalamus or pituitary are equally susceptible to injury; there is a characteristic sequence of hormonal failure. The typical order of hormone deficiency from pituitary compression or destruction is as follows: growth hormone (GH) > follicle-stimulating hormone (FSH) > luteinizing hormone (LH) > TSH > adrenocorticotropic hormone (ACTH) > vasopressin. A similar pattern is seen following brain irradiation: GH > FSH and LH > ACTH > TSH. A recent systematic review of 18 studies with 813 patients receiving cranial radiotherapy for non-pituitary tumors found pituitary dysfunction was 45% for GH deficiency, compared to 22% for ACTH deficiency.7

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.

Biochemical diagnosis of hypopituitarism consists of measuring the various pituitary and target hormone levels as well as provocation testing. When interpreting these tests, whether to identify excess or deficient states, it is important to remember the individual values are part of the broader hypothalamic-pituitary axis feedback loops. Thus, it can be more useful designating if a high or low test value is appropriately or inappropriately high or low. In the presented case, low TSH level could be misinterpreted as excess thyroid hormone supplementation. An appropriately elevated free T4 level would confirm this, but an inappropriately low free T4 would raise suspicion of central hypothalamic-pituitary dysfunction.

With high enough clinical suspicion of hypopituitarism, empiric treatment with thyroid supplementation and corticosteroids should be started before confirmation of the diagnosis, to prevent secondary organ dysfunction and improve morbidity and mortality.2 Rapid administration with intravenous levothyroxine can be given in severe hypothyroidism or myxedema.

“Stress-dose” steroids are generally recommended for patients who are also administered levothyroxine, as the desired increased in metabolic rate can deplete existing pituitary-adrenocortical hormone reserves, precipitating adrenal crisis.5 Stress-dose corticosteroids also ensure recruitment of a mineralocorticoid response. Cortisol has both GC and MC stimulating effects but is rapidly metabolized to cortisone, which lacks MC stimulating effects. Thus, high doses overwhelm this conversion step and allow remaining cortisol to stimulate MC receptors.2 These high doses may not be necessary in secondary AI (i.e., preserved MC function) but would be reasonable in an unstable patient or until confirmation is made with an inappropriately low ACTH.

Back to the Case

Morning cortisol returned undetectable, and ACTH was 14 pg/mL (6-58). Past records revealed a down-trending TSH from 1.12 to 0.38 mIU/mL, which had inappropriately prompted a levothyroxine dose reduction from 50 mcg to 25 mcg. A free thyroxine (T4) was low at 0.67 ng/dL (0.89-1.76). Estradiol, FSH, and LH were undetectable. Prolactin was 23 ng/mL (3-27). She was started on prednisone, 5 mg daily, and her levothyroxine was adjusted to a weight-based dose. Her fever resolved with the initiation of prednisone, and all cultures remained negative. Over two weeks, she improved back to her baseline, was discharged to a rehabilitation center, and eventually returned home.

 

 


Dr. Inman is a hospitalist at St. Mary’s Hospital and Regional Medical Center in Grand Junction, Colo. Dr. Bridenstine is an endocrinologist at the University of Colorado Denver. Dr. Cumbler is a hospitalist at the University of Colorado Denver.

Key Points

  • Central adrenal insufficiency lacks the hyperpigmentation and hyperkalemia associated with primary adrenal insufficiency.
  • Central adrenal insufficiency should be suspected in cases of tumors or surgery in the region of the pituitary; presentation can be delayed following intracranial radiation therapy.
  • In cases of shock due to suspected panhypopituitarism, intravenous levothyroxine should be accompanied by stress-dose steroids while awaiting laboratory confirmation.
  • When secondary (i.e., central) hormone deficiencies are suspected, check both pituitary and target organ hormones (e.g. TSH and free T4) to determine if the hypothalamic-pituitary-target organ axis is “appropriate.” Provocation testing may be necessary to confirm.

References

  1. Regal M, Pàramo C, Sierra SM, Garcia-Mayor RV. Prevalence and incidence of hypopituitarism in an adult Caucasian population in northwestern Spain. Clin Endocrinol. 2001;55(6):735-740.
  2. Bouillon R. Acute adrenal insufficiency. Endocrinol Metab Clin North Am. 2006;35(4):767-75, ix.
  3. Raff H. Glucocorticoid inhibition of neurohypophysial vasopressin secretion. Am J Physiol. 1987;252(4 Pt 2):R635-644.
  4. Erkut ZA, Pool C, Swaab DF. Glucocorticoids suppress corticotropin-releasing hormone and vasopressin expression in human hypothalamic neurons. J Clin Endocrinol Metab. 1998;83(6):2066-2073.
  5. Melmed S, Polonski KS, Reed Larsen P, Kronenberg HM. Williams Textbook of Endocrinology. 12th ed. Philadelphia, Pa.: Saunders/Elsevier; 2012.
  6. Schneider HJ, Aimaretti G, Kreitschmann-Andermahr I, Stalla GK, Ghigo E. Hypopituitarism. Lancet. 2007;369(9571):1461-1470.
  7. Appelman-Dijkstra NM, Kokshoorn NE, Dekkers OM, et al. Pituitary dysfunction in adult patients after cranial radiotherapy: systematic review and meta-analysis. J Clin Endocrinol Metabol. 2011;96(8):2330-2340.
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Impact of a Community Health Worker–Led Diabetes Education Program on Hospital and Emergency Department Utilization and Costs

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Impact of a Community Health Worker–Led Diabetes Education Program on Hospital and Emergency Department Utilization and Costs

From Baylor Scott & White Health.

 

Abstract

  • Objective: To assess the impact of a community health worker–led diabetes education program (DEP) on hospital utilization trends and to evaluate the return on investment.
  • Methods: A retrospective, pre-post study design was used to examine differences in inpatient and emergency department (ED) utilization and costs for DEP patients in the year prior to and following enrollment. Patients with diabetes who received care at the same clinics but who were not enrolled in DEP served as a control group. Analysis of covariance was used to test for differences in utilization outcomes between DEP patients and controls, controlling for age, sex, and ethnicity.
  • Results: DEP patients had a significant reduction in mean inpatient encounters (0.08 vs. 0.18), LOS per inpatient encounter (0.28 vs. 0.67), and inpatient cost per patient ($406 vs. $902) following DEP enrollment. Patients in the control group also experienced a significant reduction in inpatient LOS and costs. Neither group experienced a significant difference in ED utilization or costs. Return on investment for DEP was –66%, as the annual cost savings generated per patient from reduced utilization ($137.19) were less than the annual DEP costs (investment) per patient ($402.80).
  • Conclusion: CHW-led diabetes education programs like DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the DEP did not generate an overall cost savings for the health care system in the short term, additional savings may be generated in the long term through reductions of diabetes-related complications.

 

Diabetes poses a significant burden on the population of the United States in terms of morbidity, mortality, and costs of care. Currently, 29.1 million people in the United States have diabetes [1], and it is expected that 1 in 3 people could have diabetes by the year 2050 [2]. The economic impact of diabetes is also significant; as of 2012, 1 in 5 health care dollars went towards diabetes care [3], and the total cost of diabetes was estimated to be $245 billion [1]. This rapid increase in the incidence of diabetes and associated costs emphasizes the need for cost-effective strategies to prevent and manage diabetes within the U.S. population.

One strategy that has shown success in improving diabetes care and outcomes is the use of community health workers (CHWs) to deliver disease education and management programs. A CHW is a front-line public health worker who is often a trusted member of the community and has the capacity to influence patient understanding, enhance patient compliance, and promote more equitable practices in patient care management [4–6]. Several studies have shown that CHW-led interventions in diabetes management can help patients achieve control over their disease and improved health indicators such as HbA1c, blood pressure, lipid levels, and weight [7–17]. In addition, a few studies have shown that CHW-led interventions for patients with diabetes can help these patients avoid costly health care utilization in the form of emergency department (ED) visits and hospitalizations. Fedder et al found that diabetes patients on Medicaid who worked closely with CHWs experienced a decrease in ED visits (38%), hospitalizations (30%), and hospital admissions from ED (53%) [11]. This CHW intervention was associated with improved patient quality of life and a cost-savings of $2245 per patient per year for a total savings of $262,080 for 117 patients. More recently, a 2010 randomized controlled trial showed that African-American patients with diabetes who worked with a nurse case manager and a CHW were 23% less likely to make ED visits than those who just had a nurse case manager [18].

Despite the documented successes of CHW programs, the CHW model has not been widely adopted within integrated health care systems. A major barrier to adoption of CHW programs has been the lack of sustained funding for CHW services [17,19]. Historically, many CHW programs were supported by grants, as most payers were unwilling to fund these initiatives [4,17,19.] In 2008, the Centers for Medicare and Medicaid Services provided a mechanism to support CHW activities by approving a Medicaid state plan amendment authorizing payment for CHWs who worked under Medicaid-approved providers, such as physicians and nurses [19]. However, the lack of data available regarding the costs, cost-effectiveness, and potential costs savings of CHW programs continues to serve as a barrier to adoption [13,20].

Baylor Scott & White Health North (BSWH), formerly Baylor Health Care System in Dallas, Texas, created the CHW-led Diabetes Equity Project, a 5-year program supported with funding from Merck Foundation’s Alliance to Reduce Disparities in Diabetes, with the goal of reducing observed disparities in diabetes care and outcomes in the medically underserved, predominantly Hispanic communities surrounding BSWH hospitals [16,21,22]. The program featured specially trained, bilingual CHWs who served as members of primary care teams in 5 community clinics and delivered a culturally relevant diabetes self-management and education curriculum (DSME) targeting barriers to diabetes management commonly experienced by Hispanics. The objective of this study was to assess the impact of a CHW-led diabetes educucation program (DEP) on hospital utilization trends and to evaluate the return on investment of the program.

 

 

Methods

Setting

BSWH is one of the largest nonprofit health care systems in the United States and includes 46 hospitals, > 800 patient care sites, > 6000 affiliated physicians, 35,000 employees, and an accountable care organization. This study was conducted in 5 community clinics located in the Dallas metroplex surrounding BSWH North hospitals. The community clinics serve low-income, uninsured, and chronically ill patients. The study was approved by the Baylor Research Institute institutional review board.

DEP Intervention

The DEP program consisted of 2 initial 60-minute educational sessions and quarterly clinical assessments scheduled for 30 to 60 minutes for a maximum of 6 patient-contact hours over 12 consecutive months. The DSME curriculum for DEP was adapted from CoDE, a pilot program implemented in a Dallas clinic serving a largely uninsured Mexican American population [23]. Patients who participated in CoDE for 12 months experienced a significant reduction in HbA1c [23,24]. During the 2 educational sessions, the CHWs educated DEP participants about diabetes and the importance of blood glucose control, medication adherence, diet, and exercise. In addition to the educational sessions, CHWs performed quarterly clinical assessments of HbA1c, blood pressure, weight, and foot condition (visual and monofilament assessment). They also assessed self-management behaviors and facilitated goal setting at each visit. The CHWs documented patient visits in the electronic health record and contacted the patient’s primary care provider immediately if the patient was symptomatic or had critical blood glucose or blood pressure measurements as defined by program protocol.

Participants

Study participants were recruited from the clinics or referred by Baylor care coordinators following hospital visits related to uncontrolled diabetes from September 2009 to July 2013. Participants had to be 18 years or older with a diagnosis of type 2 diabetes and be uninsured or underinsured. Although the program targeted Hispanic patients, all patients who met the inclusion criteria were eligible to participate. To control for internal threats to validity such as history and maturation biases, we created a control group consisting of clinic patients who had a diagnosis of diabetes and met the DEP inclusion criteria but who did not enroll in DEP.

Assessment

We used a retrospective pre-post study design to assess the impact of the DEP on hospital utilization trends and the program’s return on investment (ROI). The primary outcomes were number of hospital encounters, length of stay (LOS) per encounter, and direct cost per patient. The pre-enrollment period was defined as the year priod to the day of the initial DEP visit (the date of enrollment for DEP patients) or the initial clinic visit (the date of “enrollment” for control patients). All study participants were included in the analysis regardless of their number of inpatient or ED encounters. If a participant did not have a documented encounter during the analysis time period, encounters, LOS, and medical costs were set to zero. Patients with at least 2 HbA1c measurements taken during the first year post enrollment were included in the analysis.

Data Sources

Inpatient and emergency department (ED) utilization data were obtained from the Dallas Fort-Worth Hospital Council (DFWHC) database. The DFWHC captures administrative data from over 80 participating hospital systems and 9 million unique patients in North Texas. The DFWHC applied a matching algorithm using first and last name and date of birth to match study participants with encounter and length of stay detail for all hospitalizations across all DFWHC member hospitals. We obtained direct medical costs for patients treated at BSWH facilities from the BSWH Trendstar administrative database. Direct medical cost was not available for encounters at non-BSWH facilities so cost was estimated for these patients based on BSWH costs using a prediction model that accounted for LOS, primary ICD-9 diagnosis, patient age, sex, and race. The model explained approximately 66% of the variation in direct costs (R= 0.6581).

Statistical Analysis

All analyses were performed in SAS V9.3 using an α level of 0.05. A 2-tailed independent samples t test was used to test the mean differences in utilization outcomes one year prior to and post program enrollment. An analysis of covariance (ANCOVA) was used to assess whether the mean change in utilization outcomes was greater for DEP patients than the control group, , after controlling for age, sex, and ethnicity. The gamma distribution with a log link function was used to model direct cost and the negative binomial distribution was used to model hospital encounters and LOS.

The ROI calculation included 2 components: DEP investment cost and risk-adjusted direct medical cost savings 1 year post program enrollment. DEP investment cost included the average yearly costs per community health worker, the fixed one-time start-up costs distributed across the length of the program (4 years), and the salaries of the 5 community health workers employed for the duration of the program. These costs were divided by the number of patients who enrolled in the DEP to calculate the per patient cost (investment). Direct medical cost savings were calculated as the mean reduction in hospitalization and ED costs per patient adjusted for age, sex, and ethnicity.

 

 

To account for DEP participants without encounter data, we estimated average cost savings in a 4-step process using a propensity score adjustment approach. We stratified encounters based on admission type (inpatient vs. ED) and combined the average cost savings calculated for both admission types to calculate mean total medical cost savings.

In the first step, direct cost was modeled on DEP participants with an encounter to obtain parameter estimates for pre- and post- program enrollment, age, sex, and ethnicity. The model was based on the log-link function with the gamma distribution. In the second step, we ran a propensity score logistic regression model to estimate the probability of any encounter being identified in the DFWHC database, adjusted for age, sex, ethnicity, and DEP clinic. In the third step, we generated 10,000 bootstrap samples, with replacement, to estimate the DEP population’s median expected change in cost. In the bootstrap sample, the parameter estimates from step 1 were used to predict the cost pre- and post- program enrollment for each patient. The expected change in cost per participant was then calculated, while adjusting for the propensity to have an encounter, estimated in step 2. The formula for calculating change in cost was Expected change in cost = [pre probability*pre cost – post probability*post cost].

In the final step, we retained the median cost savings from each bootstrap sample. Inpatient and ED cost savings were combined to produce a total cost savings across the patient population. The ROI formula was Financial gain (utilization cost savings – DEP investment)/DEP investment.

 

Results

Participant Demographics

Of the 1140 study participants, 878 were DEP patients and 262 were controls (Table 1). The majority of DEP patients were Hispanic females older than 40 years while the majority of the the control group was non-Hispanic males older than 40. Table 2 and Table 3 display the top 5 primary ICD-9 diagnoses by intervention group and pre/post enrollment status. Type 2 diabetes was the top diagnosis among DEP participants and controls with a pre-enrollment encounter. 

Acute pancreatitis was the top diagnosis among DEP participants and controls with a post-enrollment encounter.

Inpatient Encounters

Fourteen percent of the DEP participants and 37% of control group patients matched to an inpatient record during the pre-enrollment time period. The number of mean inpatient encounters for DEP participants decreased from 0.18 to 0.08 (P < 0.001) in the post period 

(Table 4). Patients in the control group also experienced a reduction in mean inpatient encounters (0.66 to 0.52), but this reduction was not significant. Both DEP and control group patients had a significant reduction in mean LOS in the post period. LOS decreased from 0.67 days to 0.28 days (P < 0.001) per encounter for DEP patients and from 2.32 days to 1.13 days (P = 0.001) for controls. However, the mean percentage change in LOS was not significantly different between the 2 study groups (P = 0.90). DEP patients and control patients also 
had a significant reduction in mean inpatient cost per patient, decreasing from $902 to $406 (P < 0.001) for DEP patients and from $3054 to $1688 for controls (P = 0.006). Observed reductions in cost were not significantly different between the 2 groups (P = 0.93).

Emergency Department Encounters

Eight percent of the DEP participants and 30% of controls matched to an ED record for the year prior to program enrollment. Neither DEP patients nor patients in the control group had a significant reduction in mean ED encounters (P = 0.88 and 0.74, respectively) or costs (P = 0.76 and 0.12, respectively) post enrollment.

 

ROI

The cost savings per DEP patient are shown in Table 5. The annual cost for a CHW to educate 1 DEP patient was $403. The combined inpatient and ED cost savings for DEP patients post-program enrollment was $137 per patient. ROI for the DEP was –66%, indicating that DEP investment costs were greater than the savings achieved through the reduction of inpatient and ED costs for DEP patients.

Discussion

In our examination of the impact of the DEP on inpatient and ED utilization, we found that DEP patients experienced a significant decrease in inpatient visits, LOS, and direct costs in the year following DEP enrollment. In comparison, a control group of patients who were treated at the same clinics as DEP patients also experienced significant decreases in inpatient LOS and direct costs. No significant differences in ED visits, LOS, or direct costs were observed for either DEP patients or control patients in the post period.

The reduction in inpatient visits and LOS for DEP patients in the year following DEP enrollment indicates that the DEP helped patients achieve improved health and avoid costly hospitalizations. The control group did experience greater reductions in inpatient utilization, LOS, and direct costs. However, because this was not designed as a controlled trial, we utilized a nonequivalent control group and inherent differences between the DEP and control patients and utilization patterns made it difficult to draw unbiased comparisons between the groups. For instance, the number of inpatient encounters, LOS, and costs were 2 to 3 times higher in the pre-period for the control group. The mean number of inpatient encounters for DEP patients prior to DEP participation was 0.18, and the smaller change observed in utilization for DEP patients is likely due to a floor effect.

Despite the observed reductions for both DEP patients and the control group in inpatient utilization, neither group had significant reductions in ED visits or costs per patient in the post- period. The majority of ED use in the pre-period may have been due to diabetes-related complications that are difficult to prevent even with improved diabetes care or for emergencies not related to diabetes, as we included all ED admissions regardless of admitting diagnosis. In addition, similar to observed trends in inpatient utilization, ED use in the pre-period was relatively low for DEP patients (0.16), and the lack of observed changes in ED utilization for DEP patients was also likely due to a floor effect.

The DEP generated a negative ROI (–66%) in the short term as the annual cost savings generated per patient from reduced utilization ($137) were less than the annual DEP costs (investment) per patient ($403). This finding is not surprising, as it is difficult to achieve cost savings for interventions designed to increase access to health care in underserved populations.[15] Although Fedder et al. observed an average savings of $2245 per patient per year in Medicaid reimbursements for a CHW-led outreach program for patients on Medicaid with diabetes, patients who participated in this program had much higher inpatient encounters (0.95 vs. 0.18) and ED utilization (1.49 vs. 0.16) at baseline compared to DEP patients. DEP patients may not have been as sick as these patients or have been more reluctant to seek medical care due to their lack of insurance. In addition, Fedder et al did not factor in the costs of the CHW program in the cost savings calculation. However, these costs were likely to be much lower than the costs of the DEP, as the program relied on volunteer CHWs instead of paid CHWs who were also certified medical assistants.

Several studies have evaluated the cost-effectiveness of CHW-led diabetes management programs rather than the ROI as these types of programs are often associated with improved health outcomes but also increased health care costs as the result of expanding health care access and services to underserved populations [15,25–29]. Most of these evaluations modeled the long-term cost-effectiveness, as the majority of cost savings from diabetes management programs are likely to accrue in the long run as a result of the prevention of diabetes-related complications such as amputations, blindness, kidney failure, coronary heart disease, and stroke [15,25]. Although these CHW-led interventions for diabetes differed in scope, the incremental cost effectiveness ratios for these interventions were less than the common willingness-to-pay threshold of $50,000 per quality-adjusted life year (QALY). These studies may have underestimated the societal benefits of these programs as they did not incorporate non-medical cost savings such as those that may be attributed to gains in productivity [26].

 

 

A recent cost-effectiveness analysis of the DEP and the 4 other programs that were part of the Alliance to Reduce Disparities in Diabetes found that these programs spent an average of $975 per patient in the first year and additional $520 per patient in subsequent years to improve care for diabetes patients [15]. Based on improvements in health indicators such as HbA1c, systolic blood pressure, and total cholesterol observed for participants in Alliance programs, the incremental cost-effectiveness ratio of these programs was $23,161 per QALY given the optimistic assumption that the observed improvements in health indicators were all attributable to the interventions. DEP patients achieved a 1% average reduction in mean HbA1c and this improvement was sustained over the course of the program. The UK Prospective Diabetes Study Group found that every 1% reduction in HbA1C reduces a patient’s risk of developing eye, kidney, and nerve disease by 40% and the risk of heart attack by 14% [30]. Thus, if DEP patients sustain improvements gained as a result of program participation, they may avoid serious and expensive medical complications in the future.

Ultimately, the goal of the DEP was to provide patients with improved access to diabetes care and to help them achieve improved glucose control. Improving access to chronic disease care is costly. However, employment of CHWs is a less costly alternative to employing additional clinicians, and CHWs may be more effective in assisting patients with chronic disease management particularly those from underserved and vulnerable populations. Although we did not generate a positive ROI for the DEP in this short-term analysis, the program is likely cost-effective when the low cost of the program is compared with the improvements in health outcomes we have observed.

This study had several limitations. There were observed differences in gender and ethnicity between the intervention and control groups and it is likely that risk-adjustment (including propensity scoring) did not fully accountfor underlying differences between the populations. The observed reductions in inpatient utilization and costs in the intervention group may have been due to other factors besides the DEP as the control group also achieved reductions in inpatient LOS and costs. The control group did contain a few outliers in terms of high pre-period medical costs which were retained in the analysis and these outliers may have caused reductions in costs for this group to be overstated. The observed outcomes may also be biased due to intervention contamination. Patients in the control group attended the same clinics as DEP patients and were treated by the same primary care physicians. The primary care physicians likely applied the knowledge gained from working with DEP patients and the CHWs, including how to identify and help patients address the specific barriers to diabetes self-management faced by clinic patients, to the treatment of patients who were not enrolled in the DEP. The fact that health care utilization and costs were much higher for patients in the control group in the pre-period indicates that these patients had greater severity of illness and additional comorbidities, and the observed reductions in utilization and costs for these patients may have been the result of obtaining access to a regular source of primary care through the clinics. However, the observed decrease in inpatient encounters from .18 to .08 in the post period for DEP patients as well as the observed decreases in inpatient LOS and direct costs indicate that the DEP may provide additional benefits compared to access to primary care alone in terms of avoidance costly hospitalizations for diabetes-related complications.

Conclusion

From the health care system perspective, CHW-led diabetes education programs like the DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the costs of the DEP were greater than the savings it generated through reduced inpatient utilization and costs in the short term, additional savings to the health care system or society may be generated in the long term through reductions of diabetes-related complications in patients who were able to achieve improved glycemic control through program participation. More importantly, the improvements in glycemic control achieved by DEP patients can lead to both short and long term gains in overall health and quality of life.

 

Corresponding author: Ashley Collinsworth, ScD, MPH, Scott & White Health, Center for Clinical Effectiveness, Dallas, TX 75206, [email protected].

Funding/support: This program/initiative was supported by a grant from the Merck Company Foundation through its Merck Alliance to Reduce Disparities in Diabetes program.

References

1. Centers for Disease Control and Prevention, National Diabetes Statistics Report: estimates of diabetes and its burden in the United States, 2014. US Department of Health and Human Services: Atlanta, GA.

2. Centers for Disease Control and Prevention, Diabetes Report Card 2012. US Department of Health and Human Services: Atlanta, GA.

3. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36:1033–46.

4. Balcazar H, Rosenthal EL, Brownstein JN, et al. Community health workers can be a public health force for change in the United States: three actions for a new paradigm. Am J Public Health 2011;101:2199–203.

5. Rosenthal EL, Brownstein JN, Rush CH, et al. Community health workers: part of the solution. Health Aff (Millwood) 2010;29:1338–42.

6. Viswanathan M, Kraschnewski JL, Nishikawa B, et al. Outcomes and costs of community health worker interventions: a systematic review. Med Care 2010;48:792–808.

7. Babamoto KS, Sey KA, Camilleri AJ, et al. Improving diabetes care and health measures among hispanics using community health workers: results from a randomized controlled trial. Health Educ Behav 2009;36:113–26.

8. Brown SA, Garcia AA, Kouzekanani K, Hanis CL. Culturally competent diabetes self-management education for Mexican Americans: the Starr County border health initiative. Diabetes Care 2002;25:259–68.

9. Prezio EA, Cheng D, Balasubramanian BA, et al. Community Diabetes Education (CoDE) for uninsured Mexican Americans: a randomized controlled trial of a culturally tailored diabetes education and management program led by a community health worker. Diabetes Res Clin Pract 2013;100:19–28.

10. Spencer MS, Rosland AM, Kieffer EC, et al. Effectiveness of a community health worker intervention among African American and Latino adults with type 2 diabetes: a randomized controlled trial. Am J Public Health 2011;101:2253–60.

11. Fedder DO, Chang RJ, Curry S, Nichols G. The effectiveness of a community health worker outreach program on healthcare utilization of west Baltimore City Medicaid patients with diabetes, with or without hypertension. Ethn Dis 2003;13:22–7.

12. Lorig KR, Ritter P, Stewart AL, et al. Chronic disease self-management program: 2-year health status and health care utilization outcomes. Med Care 2001;39:1217–23.

13. Whitley EM, Everhart RM, Wright RA. Measuring return on investment of outreach by community health workers. J Health Care Poor Underserved 2006;17(1 Suppl):6–15.

14. Skelly AH. Culturally tailored intervention for African Americans with type 2 diabetes administered by a nurse case manager and community health worker reduces emergency room visits. Evid Based Nurs 2010;13:51–2.

15. Lewis MA, Bann CM, Karns SA, et al. Cross-site evaluation of the Alliance to Reduce Disparities in Diabetes: clinical and patient-report
ed outcomes. Health Promot Pract 2014;15(2 Suppl):92S–102S.

16. Collinsworth AW, Vulimiri M, Schmidt KL, Snead CA. Effectiveness of a community health worker-led diabetes self-management education program and implications for CHW involvement in care coordination strategies. Diabetes Educ 2013;39:792–9.

17. Shah M, Kaselitz E, Heisler M. The role of community health workers in diabetes: update on current literature. Curr Diab Rep 2013;13:163–71.

18. Gary TL, Batts-Turner M, Yeh HC, et al. The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med 2009;169:1788–94.

19. Martinez J, Ro M, Villa NW, et al. Transforming the delivery of care in the post-health reform era: what role will community health workers play? Am J Public Health 2011;101:e1–5.

20. Rush CH. Return on investment from employment of community health workers. J Ambul Care Manage 2012;35:133–7.

21. Collinsworth A, Vulimiri M, Snead C, Walton J. Community health workers in primary care practice: redesigning health care delivery systems to extend and improve diabetes care in underserved populations. Health Promot Pract 2014;15(2 Suppl):51S–61S.

22. Walton JW, Snead CA, Collinsworth AW, Schmidt KL. Reducing diabetes disparities through the implementation of a community health worker-led diabetes self-management education program. Fam Community Health 2012;35:161–71.

23. Culica D, Walton JW, Prezio EA. CoDE: Community Diabetes Education for uninsured Mexican Americans. Proc (Bayl Univ Med Cent) 2007;20:111–7.

24. Culica D, Walton JW, Harker K, Prezio EA. Effectiveness of a community health worker as sole diabetes educator: comparison of CoDE with similar culturally appropriate interventions. J Health Care Poor Underserved 2008;19:1076–95.

25. Brownson CA, Hoerger TJ, Fisher EB, Kilpatrick KE. Cost-effectiveness of diabetes self-management programs in community primary care settings. Diabetes Educ 2009;35:761–9.

26. Gilmer TP, Roze S, Valentine WJ, et al. Cost-effectiveness of diabetes case management for low-income populations. Health Serv Res 2007;42:1943–59.

27. Brown HS 3rd, Wilson KJ, Pagán JA, et al. Cost-effectiveness analysis of a community health worker intervention for low-income Hispanic adults with diabetes. Prev Chronic Dis 2012;9:E140.

28. Ryabov I. Cost-effectiveness of community health workers in controlling diabetes epidemic on the US-Mexico border. Public Health 2014;128:636–42.

29. Prezio EA, Pagán JA, Shuval K, Culica D. The Community Diabetes Education (CoDE) program: cost-effectiveness and health outcomes. Am J Prev Med 2014;47:771–9.

30. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998;352:837–53.

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From Baylor Scott & White Health.

 

Abstract

  • Objective: To assess the impact of a community health worker–led diabetes education program (DEP) on hospital utilization trends and to evaluate the return on investment.
  • Methods: A retrospective, pre-post study design was used to examine differences in inpatient and emergency department (ED) utilization and costs for DEP patients in the year prior to and following enrollment. Patients with diabetes who received care at the same clinics but who were not enrolled in DEP served as a control group. Analysis of covariance was used to test for differences in utilization outcomes between DEP patients and controls, controlling for age, sex, and ethnicity.
  • Results: DEP patients had a significant reduction in mean inpatient encounters (0.08 vs. 0.18), LOS per inpatient encounter (0.28 vs. 0.67), and inpatient cost per patient ($406 vs. $902) following DEP enrollment. Patients in the control group also experienced a significant reduction in inpatient LOS and costs. Neither group experienced a significant difference in ED utilization or costs. Return on investment for DEP was –66%, as the annual cost savings generated per patient from reduced utilization ($137.19) were less than the annual DEP costs (investment) per patient ($402.80).
  • Conclusion: CHW-led diabetes education programs like DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the DEP did not generate an overall cost savings for the health care system in the short term, additional savings may be generated in the long term through reductions of diabetes-related complications.

 

Diabetes poses a significant burden on the population of the United States in terms of morbidity, mortality, and costs of care. Currently, 29.1 million people in the United States have diabetes [1], and it is expected that 1 in 3 people could have diabetes by the year 2050 [2]. The economic impact of diabetes is also significant; as of 2012, 1 in 5 health care dollars went towards diabetes care [3], and the total cost of diabetes was estimated to be $245 billion [1]. This rapid increase in the incidence of diabetes and associated costs emphasizes the need for cost-effective strategies to prevent and manage diabetes within the U.S. population.

One strategy that has shown success in improving diabetes care and outcomes is the use of community health workers (CHWs) to deliver disease education and management programs. A CHW is a front-line public health worker who is often a trusted member of the community and has the capacity to influence patient understanding, enhance patient compliance, and promote more equitable practices in patient care management [4–6]. Several studies have shown that CHW-led interventions in diabetes management can help patients achieve control over their disease and improved health indicators such as HbA1c, blood pressure, lipid levels, and weight [7–17]. In addition, a few studies have shown that CHW-led interventions for patients with diabetes can help these patients avoid costly health care utilization in the form of emergency department (ED) visits and hospitalizations. Fedder et al found that diabetes patients on Medicaid who worked closely with CHWs experienced a decrease in ED visits (38%), hospitalizations (30%), and hospital admissions from ED (53%) [11]. This CHW intervention was associated with improved patient quality of life and a cost-savings of $2245 per patient per year for a total savings of $262,080 for 117 patients. More recently, a 2010 randomized controlled trial showed that African-American patients with diabetes who worked with a nurse case manager and a CHW were 23% less likely to make ED visits than those who just had a nurse case manager [18].

Despite the documented successes of CHW programs, the CHW model has not been widely adopted within integrated health care systems. A major barrier to adoption of CHW programs has been the lack of sustained funding for CHW services [17,19]. Historically, many CHW programs were supported by grants, as most payers were unwilling to fund these initiatives [4,17,19.] In 2008, the Centers for Medicare and Medicaid Services provided a mechanism to support CHW activities by approving a Medicaid state plan amendment authorizing payment for CHWs who worked under Medicaid-approved providers, such as physicians and nurses [19]. However, the lack of data available regarding the costs, cost-effectiveness, and potential costs savings of CHW programs continues to serve as a barrier to adoption [13,20].

Baylor Scott & White Health North (BSWH), formerly Baylor Health Care System in Dallas, Texas, created the CHW-led Diabetes Equity Project, a 5-year program supported with funding from Merck Foundation’s Alliance to Reduce Disparities in Diabetes, with the goal of reducing observed disparities in diabetes care and outcomes in the medically underserved, predominantly Hispanic communities surrounding BSWH hospitals [16,21,22]. The program featured specially trained, bilingual CHWs who served as members of primary care teams in 5 community clinics and delivered a culturally relevant diabetes self-management and education curriculum (DSME) targeting barriers to diabetes management commonly experienced by Hispanics. The objective of this study was to assess the impact of a CHW-led diabetes educucation program (DEP) on hospital utilization trends and to evaluate the return on investment of the program.

 

 

Methods

Setting

BSWH is one of the largest nonprofit health care systems in the United States and includes 46 hospitals, > 800 patient care sites, > 6000 affiliated physicians, 35,000 employees, and an accountable care organization. This study was conducted in 5 community clinics located in the Dallas metroplex surrounding BSWH North hospitals. The community clinics serve low-income, uninsured, and chronically ill patients. The study was approved by the Baylor Research Institute institutional review board.

DEP Intervention

The DEP program consisted of 2 initial 60-minute educational sessions and quarterly clinical assessments scheduled for 30 to 60 minutes for a maximum of 6 patient-contact hours over 12 consecutive months. The DSME curriculum for DEP was adapted from CoDE, a pilot program implemented in a Dallas clinic serving a largely uninsured Mexican American population [23]. Patients who participated in CoDE for 12 months experienced a significant reduction in HbA1c [23,24]. During the 2 educational sessions, the CHWs educated DEP participants about diabetes and the importance of blood glucose control, medication adherence, diet, and exercise. In addition to the educational sessions, CHWs performed quarterly clinical assessments of HbA1c, blood pressure, weight, and foot condition (visual and monofilament assessment). They also assessed self-management behaviors and facilitated goal setting at each visit. The CHWs documented patient visits in the electronic health record and contacted the patient’s primary care provider immediately if the patient was symptomatic or had critical blood glucose or blood pressure measurements as defined by program protocol.

Participants

Study participants were recruited from the clinics or referred by Baylor care coordinators following hospital visits related to uncontrolled diabetes from September 2009 to July 2013. Participants had to be 18 years or older with a diagnosis of type 2 diabetes and be uninsured or underinsured. Although the program targeted Hispanic patients, all patients who met the inclusion criteria were eligible to participate. To control for internal threats to validity such as history and maturation biases, we created a control group consisting of clinic patients who had a diagnosis of diabetes and met the DEP inclusion criteria but who did not enroll in DEP.

Assessment

We used a retrospective pre-post study design to assess the impact of the DEP on hospital utilization trends and the program’s return on investment (ROI). The primary outcomes were number of hospital encounters, length of stay (LOS) per encounter, and direct cost per patient. The pre-enrollment period was defined as the year priod to the day of the initial DEP visit (the date of enrollment for DEP patients) or the initial clinic visit (the date of “enrollment” for control patients). All study participants were included in the analysis regardless of their number of inpatient or ED encounters. If a participant did not have a documented encounter during the analysis time period, encounters, LOS, and medical costs were set to zero. Patients with at least 2 HbA1c measurements taken during the first year post enrollment were included in the analysis.

Data Sources

Inpatient and emergency department (ED) utilization data were obtained from the Dallas Fort-Worth Hospital Council (DFWHC) database. The DFWHC captures administrative data from over 80 participating hospital systems and 9 million unique patients in North Texas. The DFWHC applied a matching algorithm using first and last name and date of birth to match study participants with encounter and length of stay detail for all hospitalizations across all DFWHC member hospitals. We obtained direct medical costs for patients treated at BSWH facilities from the BSWH Trendstar administrative database. Direct medical cost was not available for encounters at non-BSWH facilities so cost was estimated for these patients based on BSWH costs using a prediction model that accounted for LOS, primary ICD-9 diagnosis, patient age, sex, and race. The model explained approximately 66% of the variation in direct costs (R= 0.6581).

Statistical Analysis

All analyses were performed in SAS V9.3 using an α level of 0.05. A 2-tailed independent samples t test was used to test the mean differences in utilization outcomes one year prior to and post program enrollment. An analysis of covariance (ANCOVA) was used to assess whether the mean change in utilization outcomes was greater for DEP patients than the control group, , after controlling for age, sex, and ethnicity. The gamma distribution with a log link function was used to model direct cost and the negative binomial distribution was used to model hospital encounters and LOS.

The ROI calculation included 2 components: DEP investment cost and risk-adjusted direct medical cost savings 1 year post program enrollment. DEP investment cost included the average yearly costs per community health worker, the fixed one-time start-up costs distributed across the length of the program (4 years), and the salaries of the 5 community health workers employed for the duration of the program. These costs were divided by the number of patients who enrolled in the DEP to calculate the per patient cost (investment). Direct medical cost savings were calculated as the mean reduction in hospitalization and ED costs per patient adjusted for age, sex, and ethnicity.

 

 

To account for DEP participants without encounter data, we estimated average cost savings in a 4-step process using a propensity score adjustment approach. We stratified encounters based on admission type (inpatient vs. ED) and combined the average cost savings calculated for both admission types to calculate mean total medical cost savings.

In the first step, direct cost was modeled on DEP participants with an encounter to obtain parameter estimates for pre- and post- program enrollment, age, sex, and ethnicity. The model was based on the log-link function with the gamma distribution. In the second step, we ran a propensity score logistic regression model to estimate the probability of any encounter being identified in the DFWHC database, adjusted for age, sex, ethnicity, and DEP clinic. In the third step, we generated 10,000 bootstrap samples, with replacement, to estimate the DEP population’s median expected change in cost. In the bootstrap sample, the parameter estimates from step 1 were used to predict the cost pre- and post- program enrollment for each patient. The expected change in cost per participant was then calculated, while adjusting for the propensity to have an encounter, estimated in step 2. The formula for calculating change in cost was Expected change in cost = [pre probability*pre cost – post probability*post cost].

In the final step, we retained the median cost savings from each bootstrap sample. Inpatient and ED cost savings were combined to produce a total cost savings across the patient population. The ROI formula was Financial gain (utilization cost savings – DEP investment)/DEP investment.

 

Results

Participant Demographics

Of the 1140 study participants, 878 were DEP patients and 262 were controls (Table 1). The majority of DEP patients were Hispanic females older than 40 years while the majority of the the control group was non-Hispanic males older than 40. Table 2 and Table 3 display the top 5 primary ICD-9 diagnoses by intervention group and pre/post enrollment status. Type 2 diabetes was the top diagnosis among DEP participants and controls with a pre-enrollment encounter. 

Acute pancreatitis was the top diagnosis among DEP participants and controls with a post-enrollment encounter.

Inpatient Encounters

Fourteen percent of the DEP participants and 37% of control group patients matched to an inpatient record during the pre-enrollment time period. The number of mean inpatient encounters for DEP participants decreased from 0.18 to 0.08 (P < 0.001) in the post period 

(Table 4). Patients in the control group also experienced a reduction in mean inpatient encounters (0.66 to 0.52), but this reduction was not significant. Both DEP and control group patients had a significant reduction in mean LOS in the post period. LOS decreased from 0.67 days to 0.28 days (P < 0.001) per encounter for DEP patients and from 2.32 days to 1.13 days (P = 0.001) for controls. However, the mean percentage change in LOS was not significantly different between the 2 study groups (P = 0.90). DEP patients and control patients also 
had a significant reduction in mean inpatient cost per patient, decreasing from $902 to $406 (P < 0.001) for DEP patients and from $3054 to $1688 for controls (P = 0.006). Observed reductions in cost were not significantly different between the 2 groups (P = 0.93).

Emergency Department Encounters

Eight percent of the DEP participants and 30% of controls matched to an ED record for the year prior to program enrollment. Neither DEP patients nor patients in the control group had a significant reduction in mean ED encounters (P = 0.88 and 0.74, respectively) or costs (P = 0.76 and 0.12, respectively) post enrollment.

 

ROI

The cost savings per DEP patient are shown in Table 5. The annual cost for a CHW to educate 1 DEP patient was $403. The combined inpatient and ED cost savings for DEP patients post-program enrollment was $137 per patient. ROI for the DEP was –66%, indicating that DEP investment costs were greater than the savings achieved through the reduction of inpatient and ED costs for DEP patients.

Discussion

In our examination of the impact of the DEP on inpatient and ED utilization, we found that DEP patients experienced a significant decrease in inpatient visits, LOS, and direct costs in the year following DEP enrollment. In comparison, a control group of patients who were treated at the same clinics as DEP patients also experienced significant decreases in inpatient LOS and direct costs. No significant differences in ED visits, LOS, or direct costs were observed for either DEP patients or control patients in the post period.

The reduction in inpatient visits and LOS for DEP patients in the year following DEP enrollment indicates that the DEP helped patients achieve improved health and avoid costly hospitalizations. The control group did experience greater reductions in inpatient utilization, LOS, and direct costs. However, because this was not designed as a controlled trial, we utilized a nonequivalent control group and inherent differences between the DEP and control patients and utilization patterns made it difficult to draw unbiased comparisons between the groups. For instance, the number of inpatient encounters, LOS, and costs were 2 to 3 times higher in the pre-period for the control group. The mean number of inpatient encounters for DEP patients prior to DEP participation was 0.18, and the smaller change observed in utilization for DEP patients is likely due to a floor effect.

Despite the observed reductions for both DEP patients and the control group in inpatient utilization, neither group had significant reductions in ED visits or costs per patient in the post- period. The majority of ED use in the pre-period may have been due to diabetes-related complications that are difficult to prevent even with improved diabetes care or for emergencies not related to diabetes, as we included all ED admissions regardless of admitting diagnosis. In addition, similar to observed trends in inpatient utilization, ED use in the pre-period was relatively low for DEP patients (0.16), and the lack of observed changes in ED utilization for DEP patients was also likely due to a floor effect.

The DEP generated a negative ROI (–66%) in the short term as the annual cost savings generated per patient from reduced utilization ($137) were less than the annual DEP costs (investment) per patient ($403). This finding is not surprising, as it is difficult to achieve cost savings for interventions designed to increase access to health care in underserved populations.[15] Although Fedder et al. observed an average savings of $2245 per patient per year in Medicaid reimbursements for a CHW-led outreach program for patients on Medicaid with diabetes, patients who participated in this program had much higher inpatient encounters (0.95 vs. 0.18) and ED utilization (1.49 vs. 0.16) at baseline compared to DEP patients. DEP patients may not have been as sick as these patients or have been more reluctant to seek medical care due to their lack of insurance. In addition, Fedder et al did not factor in the costs of the CHW program in the cost savings calculation. However, these costs were likely to be much lower than the costs of the DEP, as the program relied on volunteer CHWs instead of paid CHWs who were also certified medical assistants.

Several studies have evaluated the cost-effectiveness of CHW-led diabetes management programs rather than the ROI as these types of programs are often associated with improved health outcomes but also increased health care costs as the result of expanding health care access and services to underserved populations [15,25–29]. Most of these evaluations modeled the long-term cost-effectiveness, as the majority of cost savings from diabetes management programs are likely to accrue in the long run as a result of the prevention of diabetes-related complications such as amputations, blindness, kidney failure, coronary heart disease, and stroke [15,25]. Although these CHW-led interventions for diabetes differed in scope, the incremental cost effectiveness ratios for these interventions were less than the common willingness-to-pay threshold of $50,000 per quality-adjusted life year (QALY). These studies may have underestimated the societal benefits of these programs as they did not incorporate non-medical cost savings such as those that may be attributed to gains in productivity [26].

 

 

A recent cost-effectiveness analysis of the DEP and the 4 other programs that were part of the Alliance to Reduce Disparities in Diabetes found that these programs spent an average of $975 per patient in the first year and additional $520 per patient in subsequent years to improve care for diabetes patients [15]. Based on improvements in health indicators such as HbA1c, systolic blood pressure, and total cholesterol observed for participants in Alliance programs, the incremental cost-effectiveness ratio of these programs was $23,161 per QALY given the optimistic assumption that the observed improvements in health indicators were all attributable to the interventions. DEP patients achieved a 1% average reduction in mean HbA1c and this improvement was sustained over the course of the program. The UK Prospective Diabetes Study Group found that every 1% reduction in HbA1C reduces a patient’s risk of developing eye, kidney, and nerve disease by 40% and the risk of heart attack by 14% [30]. Thus, if DEP patients sustain improvements gained as a result of program participation, they may avoid serious and expensive medical complications in the future.

Ultimately, the goal of the DEP was to provide patients with improved access to diabetes care and to help them achieve improved glucose control. Improving access to chronic disease care is costly. However, employment of CHWs is a less costly alternative to employing additional clinicians, and CHWs may be more effective in assisting patients with chronic disease management particularly those from underserved and vulnerable populations. Although we did not generate a positive ROI for the DEP in this short-term analysis, the program is likely cost-effective when the low cost of the program is compared with the improvements in health outcomes we have observed.

This study had several limitations. There were observed differences in gender and ethnicity between the intervention and control groups and it is likely that risk-adjustment (including propensity scoring) did not fully accountfor underlying differences between the populations. The observed reductions in inpatient utilization and costs in the intervention group may have been due to other factors besides the DEP as the control group also achieved reductions in inpatient LOS and costs. The control group did contain a few outliers in terms of high pre-period medical costs which were retained in the analysis and these outliers may have caused reductions in costs for this group to be overstated. The observed outcomes may also be biased due to intervention contamination. Patients in the control group attended the same clinics as DEP patients and were treated by the same primary care physicians. The primary care physicians likely applied the knowledge gained from working with DEP patients and the CHWs, including how to identify and help patients address the specific barriers to diabetes self-management faced by clinic patients, to the treatment of patients who were not enrolled in the DEP. The fact that health care utilization and costs were much higher for patients in the control group in the pre-period indicates that these patients had greater severity of illness and additional comorbidities, and the observed reductions in utilization and costs for these patients may have been the result of obtaining access to a regular source of primary care through the clinics. However, the observed decrease in inpatient encounters from .18 to .08 in the post period for DEP patients as well as the observed decreases in inpatient LOS and direct costs indicate that the DEP may provide additional benefits compared to access to primary care alone in terms of avoidance costly hospitalizations for diabetes-related complications.

Conclusion

From the health care system perspective, CHW-led diabetes education programs like the DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the costs of the DEP were greater than the savings it generated through reduced inpatient utilization and costs in the short term, additional savings to the health care system or society may be generated in the long term through reductions of diabetes-related complications in patients who were able to achieve improved glycemic control through program participation. More importantly, the improvements in glycemic control achieved by DEP patients can lead to both short and long term gains in overall health and quality of life.

 

Corresponding author: Ashley Collinsworth, ScD, MPH, Scott & White Health, Center for Clinical Effectiveness, Dallas, TX 75206, [email protected].

Funding/support: This program/initiative was supported by a grant from the Merck Company Foundation through its Merck Alliance to Reduce Disparities in Diabetes program.

From Baylor Scott & White Health.

 

Abstract

  • Objective: To assess the impact of a community health worker–led diabetes education program (DEP) on hospital utilization trends and to evaluate the return on investment.
  • Methods: A retrospective, pre-post study design was used to examine differences in inpatient and emergency department (ED) utilization and costs for DEP patients in the year prior to and following enrollment. Patients with diabetes who received care at the same clinics but who were not enrolled in DEP served as a control group. Analysis of covariance was used to test for differences in utilization outcomes between DEP patients and controls, controlling for age, sex, and ethnicity.
  • Results: DEP patients had a significant reduction in mean inpatient encounters (0.08 vs. 0.18), LOS per inpatient encounter (0.28 vs. 0.67), and inpatient cost per patient ($406 vs. $902) following DEP enrollment. Patients in the control group also experienced a significant reduction in inpatient LOS and costs. Neither group experienced a significant difference in ED utilization or costs. Return on investment for DEP was –66%, as the annual cost savings generated per patient from reduced utilization ($137.19) were less than the annual DEP costs (investment) per patient ($402.80).
  • Conclusion: CHW-led diabetes education programs like DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the DEP did not generate an overall cost savings for the health care system in the short term, additional savings may be generated in the long term through reductions of diabetes-related complications.

 

Diabetes poses a significant burden on the population of the United States in terms of morbidity, mortality, and costs of care. Currently, 29.1 million people in the United States have diabetes [1], and it is expected that 1 in 3 people could have diabetes by the year 2050 [2]. The economic impact of diabetes is also significant; as of 2012, 1 in 5 health care dollars went towards diabetes care [3], and the total cost of diabetes was estimated to be $245 billion [1]. This rapid increase in the incidence of diabetes and associated costs emphasizes the need for cost-effective strategies to prevent and manage diabetes within the U.S. population.

One strategy that has shown success in improving diabetes care and outcomes is the use of community health workers (CHWs) to deliver disease education and management programs. A CHW is a front-line public health worker who is often a trusted member of the community and has the capacity to influence patient understanding, enhance patient compliance, and promote more equitable practices in patient care management [4–6]. Several studies have shown that CHW-led interventions in diabetes management can help patients achieve control over their disease and improved health indicators such as HbA1c, blood pressure, lipid levels, and weight [7–17]. In addition, a few studies have shown that CHW-led interventions for patients with diabetes can help these patients avoid costly health care utilization in the form of emergency department (ED) visits and hospitalizations. Fedder et al found that diabetes patients on Medicaid who worked closely with CHWs experienced a decrease in ED visits (38%), hospitalizations (30%), and hospital admissions from ED (53%) [11]. This CHW intervention was associated with improved patient quality of life and a cost-savings of $2245 per patient per year for a total savings of $262,080 for 117 patients. More recently, a 2010 randomized controlled trial showed that African-American patients with diabetes who worked with a nurse case manager and a CHW were 23% less likely to make ED visits than those who just had a nurse case manager [18].

Despite the documented successes of CHW programs, the CHW model has not been widely adopted within integrated health care systems. A major barrier to adoption of CHW programs has been the lack of sustained funding for CHW services [17,19]. Historically, many CHW programs were supported by grants, as most payers were unwilling to fund these initiatives [4,17,19.] In 2008, the Centers for Medicare and Medicaid Services provided a mechanism to support CHW activities by approving a Medicaid state plan amendment authorizing payment for CHWs who worked under Medicaid-approved providers, such as physicians and nurses [19]. However, the lack of data available regarding the costs, cost-effectiveness, and potential costs savings of CHW programs continues to serve as a barrier to adoption [13,20].

Baylor Scott & White Health North (BSWH), formerly Baylor Health Care System in Dallas, Texas, created the CHW-led Diabetes Equity Project, a 5-year program supported with funding from Merck Foundation’s Alliance to Reduce Disparities in Diabetes, with the goal of reducing observed disparities in diabetes care and outcomes in the medically underserved, predominantly Hispanic communities surrounding BSWH hospitals [16,21,22]. The program featured specially trained, bilingual CHWs who served as members of primary care teams in 5 community clinics and delivered a culturally relevant diabetes self-management and education curriculum (DSME) targeting barriers to diabetes management commonly experienced by Hispanics. The objective of this study was to assess the impact of a CHW-led diabetes educucation program (DEP) on hospital utilization trends and to evaluate the return on investment of the program.

 

 

Methods

Setting

BSWH is one of the largest nonprofit health care systems in the United States and includes 46 hospitals, > 800 patient care sites, > 6000 affiliated physicians, 35,000 employees, and an accountable care organization. This study was conducted in 5 community clinics located in the Dallas metroplex surrounding BSWH North hospitals. The community clinics serve low-income, uninsured, and chronically ill patients. The study was approved by the Baylor Research Institute institutional review board.

DEP Intervention

The DEP program consisted of 2 initial 60-minute educational sessions and quarterly clinical assessments scheduled for 30 to 60 minutes for a maximum of 6 patient-contact hours over 12 consecutive months. The DSME curriculum for DEP was adapted from CoDE, a pilot program implemented in a Dallas clinic serving a largely uninsured Mexican American population [23]. Patients who participated in CoDE for 12 months experienced a significant reduction in HbA1c [23,24]. During the 2 educational sessions, the CHWs educated DEP participants about diabetes and the importance of blood glucose control, medication adherence, diet, and exercise. In addition to the educational sessions, CHWs performed quarterly clinical assessments of HbA1c, blood pressure, weight, and foot condition (visual and monofilament assessment). They also assessed self-management behaviors and facilitated goal setting at each visit. The CHWs documented patient visits in the electronic health record and contacted the patient’s primary care provider immediately if the patient was symptomatic or had critical blood glucose or blood pressure measurements as defined by program protocol.

Participants

Study participants were recruited from the clinics or referred by Baylor care coordinators following hospital visits related to uncontrolled diabetes from September 2009 to July 2013. Participants had to be 18 years or older with a diagnosis of type 2 diabetes and be uninsured or underinsured. Although the program targeted Hispanic patients, all patients who met the inclusion criteria were eligible to participate. To control for internal threats to validity such as history and maturation biases, we created a control group consisting of clinic patients who had a diagnosis of diabetes and met the DEP inclusion criteria but who did not enroll in DEP.

Assessment

We used a retrospective pre-post study design to assess the impact of the DEP on hospital utilization trends and the program’s return on investment (ROI). The primary outcomes were number of hospital encounters, length of stay (LOS) per encounter, and direct cost per patient. The pre-enrollment period was defined as the year priod to the day of the initial DEP visit (the date of enrollment for DEP patients) or the initial clinic visit (the date of “enrollment” for control patients). All study participants were included in the analysis regardless of their number of inpatient or ED encounters. If a participant did not have a documented encounter during the analysis time period, encounters, LOS, and medical costs were set to zero. Patients with at least 2 HbA1c measurements taken during the first year post enrollment were included in the analysis.

Data Sources

Inpatient and emergency department (ED) utilization data were obtained from the Dallas Fort-Worth Hospital Council (DFWHC) database. The DFWHC captures administrative data from over 80 participating hospital systems and 9 million unique patients in North Texas. The DFWHC applied a matching algorithm using first and last name and date of birth to match study participants with encounter and length of stay detail for all hospitalizations across all DFWHC member hospitals. We obtained direct medical costs for patients treated at BSWH facilities from the BSWH Trendstar administrative database. Direct medical cost was not available for encounters at non-BSWH facilities so cost was estimated for these patients based on BSWH costs using a prediction model that accounted for LOS, primary ICD-9 diagnosis, patient age, sex, and race. The model explained approximately 66% of the variation in direct costs (R= 0.6581).

Statistical Analysis

All analyses were performed in SAS V9.3 using an α level of 0.05. A 2-tailed independent samples t test was used to test the mean differences in utilization outcomes one year prior to and post program enrollment. An analysis of covariance (ANCOVA) was used to assess whether the mean change in utilization outcomes was greater for DEP patients than the control group, , after controlling for age, sex, and ethnicity. The gamma distribution with a log link function was used to model direct cost and the negative binomial distribution was used to model hospital encounters and LOS.

The ROI calculation included 2 components: DEP investment cost and risk-adjusted direct medical cost savings 1 year post program enrollment. DEP investment cost included the average yearly costs per community health worker, the fixed one-time start-up costs distributed across the length of the program (4 years), and the salaries of the 5 community health workers employed for the duration of the program. These costs were divided by the number of patients who enrolled in the DEP to calculate the per patient cost (investment). Direct medical cost savings were calculated as the mean reduction in hospitalization and ED costs per patient adjusted for age, sex, and ethnicity.

 

 

To account for DEP participants without encounter data, we estimated average cost savings in a 4-step process using a propensity score adjustment approach. We stratified encounters based on admission type (inpatient vs. ED) and combined the average cost savings calculated for both admission types to calculate mean total medical cost savings.

In the first step, direct cost was modeled on DEP participants with an encounter to obtain parameter estimates for pre- and post- program enrollment, age, sex, and ethnicity. The model was based on the log-link function with the gamma distribution. In the second step, we ran a propensity score logistic regression model to estimate the probability of any encounter being identified in the DFWHC database, adjusted for age, sex, ethnicity, and DEP clinic. In the third step, we generated 10,000 bootstrap samples, with replacement, to estimate the DEP population’s median expected change in cost. In the bootstrap sample, the parameter estimates from step 1 were used to predict the cost pre- and post- program enrollment for each patient. The expected change in cost per participant was then calculated, while adjusting for the propensity to have an encounter, estimated in step 2. The formula for calculating change in cost was Expected change in cost = [pre probability*pre cost – post probability*post cost].

In the final step, we retained the median cost savings from each bootstrap sample. Inpatient and ED cost savings were combined to produce a total cost savings across the patient population. The ROI formula was Financial gain (utilization cost savings – DEP investment)/DEP investment.

 

Results

Participant Demographics

Of the 1140 study participants, 878 were DEP patients and 262 were controls (Table 1). The majority of DEP patients were Hispanic females older than 40 years while the majority of the the control group was non-Hispanic males older than 40. Table 2 and Table 3 display the top 5 primary ICD-9 diagnoses by intervention group and pre/post enrollment status. Type 2 diabetes was the top diagnosis among DEP participants and controls with a pre-enrollment encounter. 

Acute pancreatitis was the top diagnosis among DEP participants and controls with a post-enrollment encounter.

Inpatient Encounters

Fourteen percent of the DEP participants and 37% of control group patients matched to an inpatient record during the pre-enrollment time period. The number of mean inpatient encounters for DEP participants decreased from 0.18 to 0.08 (P < 0.001) in the post period 

(Table 4). Patients in the control group also experienced a reduction in mean inpatient encounters (0.66 to 0.52), but this reduction was not significant. Both DEP and control group patients had a significant reduction in mean LOS in the post period. LOS decreased from 0.67 days to 0.28 days (P < 0.001) per encounter for DEP patients and from 2.32 days to 1.13 days (P = 0.001) for controls. However, the mean percentage change in LOS was not significantly different between the 2 study groups (P = 0.90). DEP patients and control patients also 
had a significant reduction in mean inpatient cost per patient, decreasing from $902 to $406 (P < 0.001) for DEP patients and from $3054 to $1688 for controls (P = 0.006). Observed reductions in cost were not significantly different between the 2 groups (P = 0.93).

Emergency Department Encounters

Eight percent of the DEP participants and 30% of controls matched to an ED record for the year prior to program enrollment. Neither DEP patients nor patients in the control group had a significant reduction in mean ED encounters (P = 0.88 and 0.74, respectively) or costs (P = 0.76 and 0.12, respectively) post enrollment.

 

ROI

The cost savings per DEP patient are shown in Table 5. The annual cost for a CHW to educate 1 DEP patient was $403. The combined inpatient and ED cost savings for DEP patients post-program enrollment was $137 per patient. ROI for the DEP was –66%, indicating that DEP investment costs were greater than the savings achieved through the reduction of inpatient and ED costs for DEP patients.

Discussion

In our examination of the impact of the DEP on inpatient and ED utilization, we found that DEP patients experienced a significant decrease in inpatient visits, LOS, and direct costs in the year following DEP enrollment. In comparison, a control group of patients who were treated at the same clinics as DEP patients also experienced significant decreases in inpatient LOS and direct costs. No significant differences in ED visits, LOS, or direct costs were observed for either DEP patients or control patients in the post period.

The reduction in inpatient visits and LOS for DEP patients in the year following DEP enrollment indicates that the DEP helped patients achieve improved health and avoid costly hospitalizations. The control group did experience greater reductions in inpatient utilization, LOS, and direct costs. However, because this was not designed as a controlled trial, we utilized a nonequivalent control group and inherent differences between the DEP and control patients and utilization patterns made it difficult to draw unbiased comparisons between the groups. For instance, the number of inpatient encounters, LOS, and costs were 2 to 3 times higher in the pre-period for the control group. The mean number of inpatient encounters for DEP patients prior to DEP participation was 0.18, and the smaller change observed in utilization for DEP patients is likely due to a floor effect.

Despite the observed reductions for both DEP patients and the control group in inpatient utilization, neither group had significant reductions in ED visits or costs per patient in the post- period. The majority of ED use in the pre-period may have been due to diabetes-related complications that are difficult to prevent even with improved diabetes care or for emergencies not related to diabetes, as we included all ED admissions regardless of admitting diagnosis. In addition, similar to observed trends in inpatient utilization, ED use in the pre-period was relatively low for DEP patients (0.16), and the lack of observed changes in ED utilization for DEP patients was also likely due to a floor effect.

The DEP generated a negative ROI (–66%) in the short term as the annual cost savings generated per patient from reduced utilization ($137) were less than the annual DEP costs (investment) per patient ($403). This finding is not surprising, as it is difficult to achieve cost savings for interventions designed to increase access to health care in underserved populations.[15] Although Fedder et al. observed an average savings of $2245 per patient per year in Medicaid reimbursements for a CHW-led outreach program for patients on Medicaid with diabetes, patients who participated in this program had much higher inpatient encounters (0.95 vs. 0.18) and ED utilization (1.49 vs. 0.16) at baseline compared to DEP patients. DEP patients may not have been as sick as these patients or have been more reluctant to seek medical care due to their lack of insurance. In addition, Fedder et al did not factor in the costs of the CHW program in the cost savings calculation. However, these costs were likely to be much lower than the costs of the DEP, as the program relied on volunteer CHWs instead of paid CHWs who were also certified medical assistants.

Several studies have evaluated the cost-effectiveness of CHW-led diabetes management programs rather than the ROI as these types of programs are often associated with improved health outcomes but also increased health care costs as the result of expanding health care access and services to underserved populations [15,25–29]. Most of these evaluations modeled the long-term cost-effectiveness, as the majority of cost savings from diabetes management programs are likely to accrue in the long run as a result of the prevention of diabetes-related complications such as amputations, blindness, kidney failure, coronary heart disease, and stroke [15,25]. Although these CHW-led interventions for diabetes differed in scope, the incremental cost effectiveness ratios for these interventions were less than the common willingness-to-pay threshold of $50,000 per quality-adjusted life year (QALY). These studies may have underestimated the societal benefits of these programs as they did not incorporate non-medical cost savings such as those that may be attributed to gains in productivity [26].

 

 

A recent cost-effectiveness analysis of the DEP and the 4 other programs that were part of the Alliance to Reduce Disparities in Diabetes found that these programs spent an average of $975 per patient in the first year and additional $520 per patient in subsequent years to improve care for diabetes patients [15]. Based on improvements in health indicators such as HbA1c, systolic blood pressure, and total cholesterol observed for participants in Alliance programs, the incremental cost-effectiveness ratio of these programs was $23,161 per QALY given the optimistic assumption that the observed improvements in health indicators were all attributable to the interventions. DEP patients achieved a 1% average reduction in mean HbA1c and this improvement was sustained over the course of the program. The UK Prospective Diabetes Study Group found that every 1% reduction in HbA1C reduces a patient’s risk of developing eye, kidney, and nerve disease by 40% and the risk of heart attack by 14% [30]. Thus, if DEP patients sustain improvements gained as a result of program participation, they may avoid serious and expensive medical complications in the future.

Ultimately, the goal of the DEP was to provide patients with improved access to diabetes care and to help them achieve improved glucose control. Improving access to chronic disease care is costly. However, employment of CHWs is a less costly alternative to employing additional clinicians, and CHWs may be more effective in assisting patients with chronic disease management particularly those from underserved and vulnerable populations. Although we did not generate a positive ROI for the DEP in this short-term analysis, the program is likely cost-effective when the low cost of the program is compared with the improvements in health outcomes we have observed.

This study had several limitations. There were observed differences in gender and ethnicity between the intervention and control groups and it is likely that risk-adjustment (including propensity scoring) did not fully accountfor underlying differences between the populations. The observed reductions in inpatient utilization and costs in the intervention group may have been due to other factors besides the DEP as the control group also achieved reductions in inpatient LOS and costs. The control group did contain a few outliers in terms of high pre-period medical costs which were retained in the analysis and these outliers may have caused reductions in costs for this group to be overstated. The observed outcomes may also be biased due to intervention contamination. Patients in the control group attended the same clinics as DEP patients and were treated by the same primary care physicians. The primary care physicians likely applied the knowledge gained from working with DEP patients and the CHWs, including how to identify and help patients address the specific barriers to diabetes self-management faced by clinic patients, to the treatment of patients who were not enrolled in the DEP. The fact that health care utilization and costs were much higher for patients in the control group in the pre-period indicates that these patients had greater severity of illness and additional comorbidities, and the observed reductions in utilization and costs for these patients may have been the result of obtaining access to a regular source of primary care through the clinics. However, the observed decrease in inpatient encounters from .18 to .08 in the post period for DEP patients as well as the observed decreases in inpatient LOS and direct costs indicate that the DEP may provide additional benefits compared to access to primary care alone in terms of avoidance costly hospitalizations for diabetes-related complications.

Conclusion

From the health care system perspective, CHW-led diabetes education programs like the DEP may provide additional benefits than can be gained from access to primary care alone in terms of avoidance of costly hospitalizations for diabetes-related complications. Although the costs of the DEP were greater than the savings it generated through reduced inpatient utilization and costs in the short term, additional savings to the health care system or society may be generated in the long term through reductions of diabetes-related complications in patients who were able to achieve improved glycemic control through program participation. More importantly, the improvements in glycemic control achieved by DEP patients can lead to both short and long term gains in overall health and quality of life.

 

Corresponding author: Ashley Collinsworth, ScD, MPH, Scott & White Health, Center for Clinical Effectiveness, Dallas, TX 75206, [email protected].

Funding/support: This program/initiative was supported by a grant from the Merck Company Foundation through its Merck Alliance to Reduce Disparities in Diabetes program.

References

1. Centers for Disease Control and Prevention, National Diabetes Statistics Report: estimates of diabetes and its burden in the United States, 2014. US Department of Health and Human Services: Atlanta, GA.

2. Centers for Disease Control and Prevention, Diabetes Report Card 2012. US Department of Health and Human Services: Atlanta, GA.

3. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36:1033–46.

4. Balcazar H, Rosenthal EL, Brownstein JN, et al. Community health workers can be a public health force for change in the United States: three actions for a new paradigm. Am J Public Health 2011;101:2199–203.

5. Rosenthal EL, Brownstein JN, Rush CH, et al. Community health workers: part of the solution. Health Aff (Millwood) 2010;29:1338–42.

6. Viswanathan M, Kraschnewski JL, Nishikawa B, et al. Outcomes and costs of community health worker interventions: a systematic review. Med Care 2010;48:792–808.

7. Babamoto KS, Sey KA, Camilleri AJ, et al. Improving diabetes care and health measures among hispanics using community health workers: results from a randomized controlled trial. Health Educ Behav 2009;36:113–26.

8. Brown SA, Garcia AA, Kouzekanani K, Hanis CL. Culturally competent diabetes self-management education for Mexican Americans: the Starr County border health initiative. Diabetes Care 2002;25:259–68.

9. Prezio EA, Cheng D, Balasubramanian BA, et al. Community Diabetes Education (CoDE) for uninsured Mexican Americans: a randomized controlled trial of a culturally tailored diabetes education and management program led by a community health worker. Diabetes Res Clin Pract 2013;100:19–28.

10. Spencer MS, Rosland AM, Kieffer EC, et al. Effectiveness of a community health worker intervention among African American and Latino adults with type 2 diabetes: a randomized controlled trial. Am J Public Health 2011;101:2253–60.

11. Fedder DO, Chang RJ, Curry S, Nichols G. The effectiveness of a community health worker outreach program on healthcare utilization of west Baltimore City Medicaid patients with diabetes, with or without hypertension. Ethn Dis 2003;13:22–7.

12. Lorig KR, Ritter P, Stewart AL, et al. Chronic disease self-management program: 2-year health status and health care utilization outcomes. Med Care 2001;39:1217–23.

13. Whitley EM, Everhart RM, Wright RA. Measuring return on investment of outreach by community health workers. J Health Care Poor Underserved 2006;17(1 Suppl):6–15.

14. Skelly AH. Culturally tailored intervention for African Americans with type 2 diabetes administered by a nurse case manager and community health worker reduces emergency room visits. Evid Based Nurs 2010;13:51–2.

15. Lewis MA, Bann CM, Karns SA, et al. Cross-site evaluation of the Alliance to Reduce Disparities in Diabetes: clinical and patient-report
ed outcomes. Health Promot Pract 2014;15(2 Suppl):92S–102S.

16. Collinsworth AW, Vulimiri M, Schmidt KL, Snead CA. Effectiveness of a community health worker-led diabetes self-management education program and implications for CHW involvement in care coordination strategies. Diabetes Educ 2013;39:792–9.

17. Shah M, Kaselitz E, Heisler M. The role of community health workers in diabetes: update on current literature. Curr Diab Rep 2013;13:163–71.

18. Gary TL, Batts-Turner M, Yeh HC, et al. The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med 2009;169:1788–94.

19. Martinez J, Ro M, Villa NW, et al. Transforming the delivery of care in the post-health reform era: what role will community health workers play? Am J Public Health 2011;101:e1–5.

20. Rush CH. Return on investment from employment of community health workers. J Ambul Care Manage 2012;35:133–7.

21. Collinsworth A, Vulimiri M, Snead C, Walton J. Community health workers in primary care practice: redesigning health care delivery systems to extend and improve diabetes care in underserved populations. Health Promot Pract 2014;15(2 Suppl):51S–61S.

22. Walton JW, Snead CA, Collinsworth AW, Schmidt KL. Reducing diabetes disparities through the implementation of a community health worker-led diabetes self-management education program. Fam Community Health 2012;35:161–71.

23. Culica D, Walton JW, Prezio EA. CoDE: Community Diabetes Education for uninsured Mexican Americans. Proc (Bayl Univ Med Cent) 2007;20:111–7.

24. Culica D, Walton JW, Harker K, Prezio EA. Effectiveness of a community health worker as sole diabetes educator: comparison of CoDE with similar culturally appropriate interventions. J Health Care Poor Underserved 2008;19:1076–95.

25. Brownson CA, Hoerger TJ, Fisher EB, Kilpatrick KE. Cost-effectiveness of diabetes self-management programs in community primary care settings. Diabetes Educ 2009;35:761–9.

26. Gilmer TP, Roze S, Valentine WJ, et al. Cost-effectiveness of diabetes case management for low-income populations. Health Serv Res 2007;42:1943–59.

27. Brown HS 3rd, Wilson KJ, Pagán JA, et al. Cost-effectiveness analysis of a community health worker intervention for low-income Hispanic adults with diabetes. Prev Chronic Dis 2012;9:E140.

28. Ryabov I. Cost-effectiveness of community health workers in controlling diabetes epidemic on the US-Mexico border. Public Health 2014;128:636–42.

29. Prezio EA, Pagán JA, Shuval K, Culica D. The Community Diabetes Education (CoDE) program: cost-effectiveness and health outcomes. Am J Prev Med 2014;47:771–9.

30. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998;352:837–53.

References

1. Centers for Disease Control and Prevention, National Diabetes Statistics Report: estimates of diabetes and its burden in the United States, 2014. US Department of Health and Human Services: Atlanta, GA.

2. Centers for Disease Control and Prevention, Diabetes Report Card 2012. US Department of Health and Human Services: Atlanta, GA.

3. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36:1033–46.

4. Balcazar H, Rosenthal EL, Brownstein JN, et al. Community health workers can be a public health force for change in the United States: three actions for a new paradigm. Am J Public Health 2011;101:2199–203.

5. Rosenthal EL, Brownstein JN, Rush CH, et al. Community health workers: part of the solution. Health Aff (Millwood) 2010;29:1338–42.

6. Viswanathan M, Kraschnewski JL, Nishikawa B, et al. Outcomes and costs of community health worker interventions: a systematic review. Med Care 2010;48:792–808.

7. Babamoto KS, Sey KA, Camilleri AJ, et al. Improving diabetes care and health measures among hispanics using community health workers: results from a randomized controlled trial. Health Educ Behav 2009;36:113–26.

8. Brown SA, Garcia AA, Kouzekanani K, Hanis CL. Culturally competent diabetes self-management education for Mexican Americans: the Starr County border health initiative. Diabetes Care 2002;25:259–68.

9. Prezio EA, Cheng D, Balasubramanian BA, et al. Community Diabetes Education (CoDE) for uninsured Mexican Americans: a randomized controlled trial of a culturally tailored diabetes education and management program led by a community health worker. Diabetes Res Clin Pract 2013;100:19–28.

10. Spencer MS, Rosland AM, Kieffer EC, et al. Effectiveness of a community health worker intervention among African American and Latino adults with type 2 diabetes: a randomized controlled trial. Am J Public Health 2011;101:2253–60.

11. Fedder DO, Chang RJ, Curry S, Nichols G. The effectiveness of a community health worker outreach program on healthcare utilization of west Baltimore City Medicaid patients with diabetes, with or without hypertension. Ethn Dis 2003;13:22–7.

12. Lorig KR, Ritter P, Stewart AL, et al. Chronic disease self-management program: 2-year health status and health care utilization outcomes. Med Care 2001;39:1217–23.

13. Whitley EM, Everhart RM, Wright RA. Measuring return on investment of outreach by community health workers. J Health Care Poor Underserved 2006;17(1 Suppl):6–15.

14. Skelly AH. Culturally tailored intervention for African Americans with type 2 diabetes administered by a nurse case manager and community health worker reduces emergency room visits. Evid Based Nurs 2010;13:51–2.

15. Lewis MA, Bann CM, Karns SA, et al. Cross-site evaluation of the Alliance to Reduce Disparities in Diabetes: clinical and patient-report
ed outcomes. Health Promot Pract 2014;15(2 Suppl):92S–102S.

16. Collinsworth AW, Vulimiri M, Schmidt KL, Snead CA. Effectiveness of a community health worker-led diabetes self-management education program and implications for CHW involvement in care coordination strategies. Diabetes Educ 2013;39:792–9.

17. Shah M, Kaselitz E, Heisler M. The role of community health workers in diabetes: update on current literature. Curr Diab Rep 2013;13:163–71.

18. Gary TL, Batts-Turner M, Yeh HC, et al. The effects of a nurse case manager and a community health worker team on diabetic control, emergency department visits, and hospitalizations among urban African Americans with type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med 2009;169:1788–94.

19. Martinez J, Ro M, Villa NW, et al. Transforming the delivery of care in the post-health reform era: what role will community health workers play? Am J Public Health 2011;101:e1–5.

20. Rush CH. Return on investment from employment of community health workers. J Ambul Care Manage 2012;35:133–7.

21. Collinsworth A, Vulimiri M, Snead C, Walton J. Community health workers in primary care practice: redesigning health care delivery systems to extend and improve diabetes care in underserved populations. Health Promot Pract 2014;15(2 Suppl):51S–61S.

22. Walton JW, Snead CA, Collinsworth AW, Schmidt KL. Reducing diabetes disparities through the implementation of a community health worker-led diabetes self-management education program. Fam Community Health 2012;35:161–71.

23. Culica D, Walton JW, Prezio EA. CoDE: Community Diabetes Education for uninsured Mexican Americans. Proc (Bayl Univ Med Cent) 2007;20:111–7.

24. Culica D, Walton JW, Harker K, Prezio EA. Effectiveness of a community health worker as sole diabetes educator: comparison of CoDE with similar culturally appropriate interventions. J Health Care Poor Underserved 2008;19:1076–95.

25. Brownson CA, Hoerger TJ, Fisher EB, Kilpatrick KE. Cost-effectiveness of diabetes self-management programs in community primary care settings. Diabetes Educ 2009;35:761–9.

26. Gilmer TP, Roze S, Valentine WJ, et al. Cost-effectiveness of diabetes case management for low-income populations. Health Serv Res 2007;42:1943–59.

27. Brown HS 3rd, Wilson KJ, Pagán JA, et al. Cost-effectiveness analysis of a community health worker intervention for low-income Hispanic adults with diabetes. Prev Chronic Dis 2012;9:E140.

28. Ryabov I. Cost-effectiveness of community health workers in controlling diabetes epidemic on the US-Mexico border. Public Health 2014;128:636–42.

29. Prezio EA, Pagán JA, Shuval K, Culica D. The Community Diabetes Education (CoDE) program: cost-effectiveness and health outcomes. Am J Prev Med 2014;47:771–9.

30. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 1998;352:837–53.

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NASPAG: Teens largely support OTC oral contraceptive access

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NASPAG: Teens largely support OTC oral contraceptive access

ORLANDO – Most adolescents expressed interest in and support for access to over-the-counter oral contraceptives, a survey found.

Data from a separate cross-sectional study showed that adolescents are skilled at self-screening for contraindications to combined OCs, demonstrating preliminary support for the safety of over-the-counter access.

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Of 348 girls aged 14-17 years who completed the survey in the first study, 73% reported supporting over-the-counter (OTC) OC access for teens, and 61% reported being likely to use OCs if they were available OTC. Significant associations were found between support for OTC access and having used birth control and having had sex. An association was also found between the likelihood of OTC use and having used birth control, having had sex, and being white, Ruth Manski of the Jane Fonda Center for Adolescent Reproductive Health, Atlanta, reported at the annual meeting of the North American Society for Pediatric and Adolescent Gynecology.

Those who had never been tested for sexually transmitted infections were more likely to support OTC access (91% vs. 76%).

Support for, and the likelihood of, using OTC OCs were not influenced by age, geographic region, rural/urban location, health insurance status, or pregnancy history. Race played a factor in that white participants were more likely than nonwhite participants to be interested in OTC access. The study also showed that participants understood an average of 7.1 of 8 key product-labeling concepts, and that most would be willing to pay up to $20 per month for OTC OCs, with the largest percentage (42%) willing to pay between $11 and $20. About a third said they would pay $21 or more.

Survey respondents were girls recruited via Facebook advertisements in 2014. Nearly a third (32%) were aged 17 years, 31% were aged 16 years, 24% were aged 15, and 13% were aged 14. Most (79%) were white, Ms. Manski said, adding that the respondents represented 44 states, 53% lived in a suburban area, 41% had private health insurance, and 33% had public health insurance.

About 90% reported having used contraception, and 44% reported having had sex. Of those who had sex, 60% reported having had unprotected sex, 58% used OCs, and 12% reported having been pregnant.

“So results from this study show that participants are supportive of over-the-counter access and that they’re interested in obtaining oral contraceptives over the counter. The findings also suggest that teenagers understand how to use oral contraceptives based on independent label review, which offers some evidence in response to concerns raised in other studies about teenagers’ ability to understand how to use over-the-counter products,” Ms. Manski said.

The findings suggest that while cost is a concern for teens and could impact their contraceptive choices, making OCs available OTC without age restrictions may help increase adolescents’ contraceptive access and use, she said, noting that this is important given the unique barriers that adolescents face with respect to accessing contraception.

The high levels of interest, and the perception of benefit, highlight the potential of this strategy to increase contraceptive access and reduce unintended pregnancy, she added.

Although some respondents expressed concerns about safety and the potential impact on sexual behavior, the evidence with respect to currently available OTC emergency contraception demonstrates that easier access does not increase sexual risk taking, and that teens can safely use OTC emergency contraception, she said.

However, the behavioral effects of OTC OC availability are not known and should be evaluated in actual use studies, Ms. Manski added.

Concerns about the safety of OTC OC access were specifically addressed in the second study, which sought to determine whether adolescents are capable of self-screening for contraindications to OC use.

Prior studies have shown that adults are able to self-screen adequately, but little is known about adolescents’ ability to do so, Dr. Rebekah L. Williams of Riley Hospital for Children, Indianapolis, said at the meeting.

Findings in the first 61 teens aged 14-21 years enrolled in the study showed that the teens were actually more likely than their providers to report potential contraindications.

The teens completed screening questionnaires prior to their office visit, and the provider completed the medical history questionnaire after the visit.

Perfectly concordant responses between providers and patients were seen for six potential contraindications: diabetes (which was present in two cases) and heavy smoking, breastfeeding, wheelchair use, surgery within 4 weeks, and current HIV medication use (which were not present in any cases). Discordant responses were seen for the remaining potential contraindications, including breast cancer, liver disease, medication interactions, smoking, migraine, hypertension, gallbladder disease, thromboembolism/heart disease, having a first-degree relative with thromboembolism, weight over 200 pounds, and having been told/having the perception that OCs should not be used.

 

 

Potential contraindications reported only by the participant were breast cancer, liver disease, medication interaction, any smoking, hypertension, personal history of thromboembolism or heart disease, and having a first-degree relative with thromboembolism.

Participants were adolescents with a mean age of 17 years from a primary care medicine clinic in a Midwest urban setting. Of the 61 subjects, 62% were black, 62% reported having ever used contraceptives, 44% were currently using contraceptives, and 62% said they would be interested in OTC OC access. Among the 38 who reported having penile/vaginal sex, 87% had ever used OCs, and 60% were currently using them.

“In summary, adolescent women are interested in over-the-counter access to combined oral contraceptive pills, they’re skilled at self-screening, and they’re mostly more likely than providers to report potential contraindications. So these data really provide some preliminary support for the over-the-counter provision of oral contraceptive pills to adolescents,” Dr. Williams said, noting that additional research is needed to assess adolescents’ ability to use combined oral contraceptive pills correctly.

In addition, larger studies in groups with a higher prevalence of contraindications are needed, as are studies with recruitment from nonclinical settings and more diverse populations, she said.

Ms. Manski and Dr. Williams both reported having no relevant financial disclosures.

[email protected]

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ORLANDO – Most adolescents expressed interest in and support for access to over-the-counter oral contraceptives, a survey found.

Data from a separate cross-sectional study showed that adolescents are skilled at self-screening for contraindications to combined OCs, demonstrating preliminary support for the safety of over-the-counter access.

Fuse/ThinkStockPhotos.com

Of 348 girls aged 14-17 years who completed the survey in the first study, 73% reported supporting over-the-counter (OTC) OC access for teens, and 61% reported being likely to use OCs if they were available OTC. Significant associations were found between support for OTC access and having used birth control and having had sex. An association was also found between the likelihood of OTC use and having used birth control, having had sex, and being white, Ruth Manski of the Jane Fonda Center for Adolescent Reproductive Health, Atlanta, reported at the annual meeting of the North American Society for Pediatric and Adolescent Gynecology.

Those who had never been tested for sexually transmitted infections were more likely to support OTC access (91% vs. 76%).

Support for, and the likelihood of, using OTC OCs were not influenced by age, geographic region, rural/urban location, health insurance status, or pregnancy history. Race played a factor in that white participants were more likely than nonwhite participants to be interested in OTC access. The study also showed that participants understood an average of 7.1 of 8 key product-labeling concepts, and that most would be willing to pay up to $20 per month for OTC OCs, with the largest percentage (42%) willing to pay between $11 and $20. About a third said they would pay $21 or more.

Survey respondents were girls recruited via Facebook advertisements in 2014. Nearly a third (32%) were aged 17 years, 31% were aged 16 years, 24% were aged 15, and 13% were aged 14. Most (79%) were white, Ms. Manski said, adding that the respondents represented 44 states, 53% lived in a suburban area, 41% had private health insurance, and 33% had public health insurance.

About 90% reported having used contraception, and 44% reported having had sex. Of those who had sex, 60% reported having had unprotected sex, 58% used OCs, and 12% reported having been pregnant.

“So results from this study show that participants are supportive of over-the-counter access and that they’re interested in obtaining oral contraceptives over the counter. The findings also suggest that teenagers understand how to use oral contraceptives based on independent label review, which offers some evidence in response to concerns raised in other studies about teenagers’ ability to understand how to use over-the-counter products,” Ms. Manski said.

The findings suggest that while cost is a concern for teens and could impact their contraceptive choices, making OCs available OTC without age restrictions may help increase adolescents’ contraceptive access and use, she said, noting that this is important given the unique barriers that adolescents face with respect to accessing contraception.

The high levels of interest, and the perception of benefit, highlight the potential of this strategy to increase contraceptive access and reduce unintended pregnancy, she added.

Although some respondents expressed concerns about safety and the potential impact on sexual behavior, the evidence with respect to currently available OTC emergency contraception demonstrates that easier access does not increase sexual risk taking, and that teens can safely use OTC emergency contraception, she said.

However, the behavioral effects of OTC OC availability are not known and should be evaluated in actual use studies, Ms. Manski added.

Concerns about the safety of OTC OC access were specifically addressed in the second study, which sought to determine whether adolescents are capable of self-screening for contraindications to OC use.

Prior studies have shown that adults are able to self-screen adequately, but little is known about adolescents’ ability to do so, Dr. Rebekah L. Williams of Riley Hospital for Children, Indianapolis, said at the meeting.

Findings in the first 61 teens aged 14-21 years enrolled in the study showed that the teens were actually more likely than their providers to report potential contraindications.

The teens completed screening questionnaires prior to their office visit, and the provider completed the medical history questionnaire after the visit.

Perfectly concordant responses between providers and patients were seen for six potential contraindications: diabetes (which was present in two cases) and heavy smoking, breastfeeding, wheelchair use, surgery within 4 weeks, and current HIV medication use (which were not present in any cases). Discordant responses were seen for the remaining potential contraindications, including breast cancer, liver disease, medication interactions, smoking, migraine, hypertension, gallbladder disease, thromboembolism/heart disease, having a first-degree relative with thromboembolism, weight over 200 pounds, and having been told/having the perception that OCs should not be used.

 

 

Potential contraindications reported only by the participant were breast cancer, liver disease, medication interaction, any smoking, hypertension, personal history of thromboembolism or heart disease, and having a first-degree relative with thromboembolism.

Participants were adolescents with a mean age of 17 years from a primary care medicine clinic in a Midwest urban setting. Of the 61 subjects, 62% were black, 62% reported having ever used contraceptives, 44% were currently using contraceptives, and 62% said they would be interested in OTC OC access. Among the 38 who reported having penile/vaginal sex, 87% had ever used OCs, and 60% were currently using them.

“In summary, adolescent women are interested in over-the-counter access to combined oral contraceptive pills, they’re skilled at self-screening, and they’re mostly more likely than providers to report potential contraindications. So these data really provide some preliminary support for the over-the-counter provision of oral contraceptive pills to adolescents,” Dr. Williams said, noting that additional research is needed to assess adolescents’ ability to use combined oral contraceptive pills correctly.

In addition, larger studies in groups with a higher prevalence of contraindications are needed, as are studies with recruitment from nonclinical settings and more diverse populations, she said.

Ms. Manski and Dr. Williams both reported having no relevant financial disclosures.

[email protected]

ORLANDO – Most adolescents expressed interest in and support for access to over-the-counter oral contraceptives, a survey found.

Data from a separate cross-sectional study showed that adolescents are skilled at self-screening for contraindications to combined OCs, demonstrating preliminary support for the safety of over-the-counter access.

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Of 348 girls aged 14-17 years who completed the survey in the first study, 73% reported supporting over-the-counter (OTC) OC access for teens, and 61% reported being likely to use OCs if they were available OTC. Significant associations were found between support for OTC access and having used birth control and having had sex. An association was also found between the likelihood of OTC use and having used birth control, having had sex, and being white, Ruth Manski of the Jane Fonda Center for Adolescent Reproductive Health, Atlanta, reported at the annual meeting of the North American Society for Pediatric and Adolescent Gynecology.

Those who had never been tested for sexually transmitted infections were more likely to support OTC access (91% vs. 76%).

Support for, and the likelihood of, using OTC OCs were not influenced by age, geographic region, rural/urban location, health insurance status, or pregnancy history. Race played a factor in that white participants were more likely than nonwhite participants to be interested in OTC access. The study also showed that participants understood an average of 7.1 of 8 key product-labeling concepts, and that most would be willing to pay up to $20 per month for OTC OCs, with the largest percentage (42%) willing to pay between $11 and $20. About a third said they would pay $21 or more.

Survey respondents were girls recruited via Facebook advertisements in 2014. Nearly a third (32%) were aged 17 years, 31% were aged 16 years, 24% were aged 15, and 13% were aged 14. Most (79%) were white, Ms. Manski said, adding that the respondents represented 44 states, 53% lived in a suburban area, 41% had private health insurance, and 33% had public health insurance.

About 90% reported having used contraception, and 44% reported having had sex. Of those who had sex, 60% reported having had unprotected sex, 58% used OCs, and 12% reported having been pregnant.

“So results from this study show that participants are supportive of over-the-counter access and that they’re interested in obtaining oral contraceptives over the counter. The findings also suggest that teenagers understand how to use oral contraceptives based on independent label review, which offers some evidence in response to concerns raised in other studies about teenagers’ ability to understand how to use over-the-counter products,” Ms. Manski said.

The findings suggest that while cost is a concern for teens and could impact their contraceptive choices, making OCs available OTC without age restrictions may help increase adolescents’ contraceptive access and use, she said, noting that this is important given the unique barriers that adolescents face with respect to accessing contraception.

The high levels of interest, and the perception of benefit, highlight the potential of this strategy to increase contraceptive access and reduce unintended pregnancy, she added.

Although some respondents expressed concerns about safety and the potential impact on sexual behavior, the evidence with respect to currently available OTC emergency contraception demonstrates that easier access does not increase sexual risk taking, and that teens can safely use OTC emergency contraception, she said.

However, the behavioral effects of OTC OC availability are not known and should be evaluated in actual use studies, Ms. Manski added.

Concerns about the safety of OTC OC access were specifically addressed in the second study, which sought to determine whether adolescents are capable of self-screening for contraindications to OC use.

Prior studies have shown that adults are able to self-screen adequately, but little is known about adolescents’ ability to do so, Dr. Rebekah L. Williams of Riley Hospital for Children, Indianapolis, said at the meeting.

Findings in the first 61 teens aged 14-21 years enrolled in the study showed that the teens were actually more likely than their providers to report potential contraindications.

The teens completed screening questionnaires prior to their office visit, and the provider completed the medical history questionnaire after the visit.

Perfectly concordant responses between providers and patients were seen for six potential contraindications: diabetes (which was present in two cases) and heavy smoking, breastfeeding, wheelchair use, surgery within 4 weeks, and current HIV medication use (which were not present in any cases). Discordant responses were seen for the remaining potential contraindications, including breast cancer, liver disease, medication interactions, smoking, migraine, hypertension, gallbladder disease, thromboembolism/heart disease, having a first-degree relative with thromboembolism, weight over 200 pounds, and having been told/having the perception that OCs should not be used.

 

 

Potential contraindications reported only by the participant were breast cancer, liver disease, medication interaction, any smoking, hypertension, personal history of thromboembolism or heart disease, and having a first-degree relative with thromboembolism.

Participants were adolescents with a mean age of 17 years from a primary care medicine clinic in a Midwest urban setting. Of the 61 subjects, 62% were black, 62% reported having ever used contraceptives, 44% were currently using contraceptives, and 62% said they would be interested in OTC OC access. Among the 38 who reported having penile/vaginal sex, 87% had ever used OCs, and 60% were currently using them.

“In summary, adolescent women are interested in over-the-counter access to combined oral contraceptive pills, they’re skilled at self-screening, and they’re mostly more likely than providers to report potential contraindications. So these data really provide some preliminary support for the over-the-counter provision of oral contraceptive pills to adolescents,” Dr. Williams said, noting that additional research is needed to assess adolescents’ ability to use combined oral contraceptive pills correctly.

In addition, larger studies in groups with a higher prevalence of contraindications are needed, as are studies with recruitment from nonclinical settings and more diverse populations, she said.

Ms. Manski and Dr. Williams both reported having no relevant financial disclosures.

[email protected]

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AT THE NASPAG ANNUAL MEETING

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Key clinical point: Adolescents have an interest in access to over-the-counter OCs and appear capable of using them safely.

Major finding: 73% reported supporting OTC access for teens.

Data source: A survey of 348 subjects and a cross-sectional study involving 61 subjects.

Disclosures: Ms. Manski and Dr. Williams both reported having no relevant financial disclosures.

Cancer rate doubles in HCV patients

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Cancer rate doubles in HCV patients

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VIENNA, AUSTRIA—A 5-year retrospective study has shown that the cancer rate in patients with hepatitis C virus (HCV) is about double that for people without HCV, even when liver cancer is excluded.

And when liver cancer is included, the rate increases to 2.5 times higher in people with HCV.

Researchers presented these findings at the International Liver Congress 2015 as abstract 0058.

The team reviewed patient records from 2008 to 2012 at Kaiser Permanente Southern California, recording all cancer diagnoses in patients 18 years or older with or without HCV.

During the 5-year time period, there were 145,210 patient years in the HCV cohort and 13,948,826 patient years in the non-HCV cohort.

The mean age at cancer diagnosis was 61.8 in the HCV cohort and 63.5 in the non-HCV cohort.

Researchers recorded 2213 cancer diagnoses in the HCV cohort (1524/100,000). This number decreased to 1654 when they excluded liver cancer (1139/100,000).

In the non-HCV cohort, they recorded 84,419 cancer diagnoses (605/100,000), which decreased to 83,795 when liver cancer was excluded (601/100,000).

Cancer types known to be associated with HCV include non-Hodgkin lymphoma (NHL), renal and prostate cancers, and liver cancer.

NHL occurred 3.63 times more frequently in patients with HCV than in those without HCV, and myeloma occurred 2.93 times more frequently.

Renal cancer occurred 3.27 times and prostate cancer 1.98 times more frequently in patients with HCV than in those without.

“The results suggest that cancer rates are increased in the cohort of hepatitis C patients versus the non-hepatitis C patients, both including and excluding liver cancers,” said senior study author Lisa Nyberg, MD, MPH, of Kaiser Permanente.

“These findings certainly point to the suggestion that hepatitis C may be associated with an increased risk of cancer.”

However, she added that the findings “must be interpreted with caution, as the study also showed that confounding factors such as alcohol abuse, tobacco, obesity, and diabetes modified the results.”

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Photomicrograph of liver

tissue with active HCV

Photo: Sutter Health

VIENNA, AUSTRIA—A 5-year retrospective study has shown that the cancer rate in patients with hepatitis C virus (HCV) is about double that for people without HCV, even when liver cancer is excluded.

And when liver cancer is included, the rate increases to 2.5 times higher in people with HCV.

Researchers presented these findings at the International Liver Congress 2015 as abstract 0058.

The team reviewed patient records from 2008 to 2012 at Kaiser Permanente Southern California, recording all cancer diagnoses in patients 18 years or older with or without HCV.

During the 5-year time period, there were 145,210 patient years in the HCV cohort and 13,948,826 patient years in the non-HCV cohort.

The mean age at cancer diagnosis was 61.8 in the HCV cohort and 63.5 in the non-HCV cohort.

Researchers recorded 2213 cancer diagnoses in the HCV cohort (1524/100,000). This number decreased to 1654 when they excluded liver cancer (1139/100,000).

In the non-HCV cohort, they recorded 84,419 cancer diagnoses (605/100,000), which decreased to 83,795 when liver cancer was excluded (601/100,000).

Cancer types known to be associated with HCV include non-Hodgkin lymphoma (NHL), renal and prostate cancers, and liver cancer.

NHL occurred 3.63 times more frequently in patients with HCV than in those without HCV, and myeloma occurred 2.93 times more frequently.

Renal cancer occurred 3.27 times and prostate cancer 1.98 times more frequently in patients with HCV than in those without.

“The results suggest that cancer rates are increased in the cohort of hepatitis C patients versus the non-hepatitis C patients, both including and excluding liver cancers,” said senior study author Lisa Nyberg, MD, MPH, of Kaiser Permanente.

“These findings certainly point to the suggestion that hepatitis C may be associated with an increased risk of cancer.”

However, she added that the findings “must be interpreted with caution, as the study also showed that confounding factors such as alcohol abuse, tobacco, obesity, and diabetes modified the results.”

Photomicrograph of liver

tissue with active HCV

Photo: Sutter Health

VIENNA, AUSTRIA—A 5-year retrospective study has shown that the cancer rate in patients with hepatitis C virus (HCV) is about double that for people without HCV, even when liver cancer is excluded.

And when liver cancer is included, the rate increases to 2.5 times higher in people with HCV.

Researchers presented these findings at the International Liver Congress 2015 as abstract 0058.

The team reviewed patient records from 2008 to 2012 at Kaiser Permanente Southern California, recording all cancer diagnoses in patients 18 years or older with or without HCV.

During the 5-year time period, there were 145,210 patient years in the HCV cohort and 13,948,826 patient years in the non-HCV cohort.

The mean age at cancer diagnosis was 61.8 in the HCV cohort and 63.5 in the non-HCV cohort.

Researchers recorded 2213 cancer diagnoses in the HCV cohort (1524/100,000). This number decreased to 1654 when they excluded liver cancer (1139/100,000).

In the non-HCV cohort, they recorded 84,419 cancer diagnoses (605/100,000), which decreased to 83,795 when liver cancer was excluded (601/100,000).

Cancer types known to be associated with HCV include non-Hodgkin lymphoma (NHL), renal and prostate cancers, and liver cancer.

NHL occurred 3.63 times more frequently in patients with HCV than in those without HCV, and myeloma occurred 2.93 times more frequently.

Renal cancer occurred 3.27 times and prostate cancer 1.98 times more frequently in patients with HCV than in those without.

“The results suggest that cancer rates are increased in the cohort of hepatitis C patients versus the non-hepatitis C patients, both including and excluding liver cancers,” said senior study author Lisa Nyberg, MD, MPH, of Kaiser Permanente.

“These findings certainly point to the suggestion that hepatitis C may be associated with an increased risk of cancer.”

However, she added that the findings “must be interpreted with caution, as the study also showed that confounding factors such as alcohol abuse, tobacco, obesity, and diabetes modified the results.”

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