Hospital Medicine

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

Fort Belvoir Community Hospital surgeons have performed the first small-incision lenticule extraction (SMILE) in the DoD, according to Health.mil News. The procedure to reduce or eliminate nearsightedness has been performed since 2011; the FDA recently approved it for the U.S.

The very fast and short-pulsed (femtosecond) laser creates a thin disc within the cornea, which is removed through a cut on the corneal surface. Removing the tissue changes the shape of the cornea.

The procedure takes 15 to 20 minutes with the laser activated for about 90 seconds per eye. Both eyes can be treated in the same session. With SMILE, unlike LASIK, no tissue is vaporized, meaning wound recovery time is faster, and with no corneal flap created, there is no risk of flap dislocation.

A clinical study found complications were rare, and by month 12 postsurgery there were only 4 reports of moderate or severe glare and 1 of moderate or severe halos. The most commonly reported effects were starbursts, blurred vision, and difficulty judging distance or depth perception, but at 12 months, more patients reported improvement than worsening. At the 6-month follow-up, 287 of 328 patients were seeing 20/20 or better without glasses.

“We are thrilled to extend this treatment option to active duty service members under the Warfighter Refractive Eye Surgery Program,” said U.S. Army Lt. Col. Bruce Rivers, director of the program at Belvoir Hospital.

The surgery will make a difference for patients. One of the first to have it done, Navy Petty Officer 1st Class Christopher Mahmood, a submarine mechanic, said, “On a submarine we have to be able to put our breathing equipment on in approximately 30 seconds, in case of emergency. Glasses make this difficult. Getting this surgery means I have one less thing to worry about while deployed and can focus 100 percent on the mission.”

The SMILE procedure will be available at Belvoir Hospital, San Diego Naval Medical Center, and Wilford Hall in San Antonio.

Fort Belvoir Community Hospital surgeons have performed the first small-incision lenticule extraction (SMILE) in the DoD, according to Health.mil News. The procedure to reduce or eliminate nearsightedness has been performed since 2011; the FDA recently approved it for the U.S.

The very fast and short-pulsed (femtosecond) laser creates a thin disc within the cornea, which is removed through a cut on the corneal surface. Removing the tissue changes the shape of the cornea.

The procedure takes 15 to 20 minutes with the laser activated for about 90 seconds per eye. Both eyes can be treated in the same session. With SMILE, unlike LASIK, no tissue is vaporized, meaning wound recovery time is faster, and with no corneal flap created, there is no risk of flap dislocation.

A clinical study found complications were rare, and by month 12 postsurgery there were only 4 reports of moderate or severe glare and 1 of moderate or severe halos. The most commonly reported effects were starbursts, blurred vision, and difficulty judging distance or depth perception, but at 12 months, more patients reported improvement than worsening. At the 6-month follow-up, 287 of 328 patients were seeing 20/20 or better without glasses.

“We are thrilled to extend this treatment option to active duty service members under the Warfighter Refractive Eye Surgery Program,” said U.S. Army Lt. Col. Bruce Rivers, director of the program at Belvoir Hospital.

The surgery will make a difference for patients. One of the first to have it done, Navy Petty Officer 1st Class Christopher Mahmood, a submarine mechanic, said, “On a submarine we have to be able to put our breathing equipment on in approximately 30 seconds, in case of emergency. Glasses make this difficult. Getting this surgery means I have one less thing to worry about while deployed and can focus 100 percent on the mission.”

The SMILE procedure will be available at Belvoir Hospital, San Diego Naval Medical Center, and Wilford Hall in San Antonio.

Fort Belvoir Community Hospital surgeons have performed the first small-incision lenticule extraction (SMILE) in the DoD, according to Health.mil News. The procedure to reduce or eliminate nearsightedness has been performed since 2011; the FDA recently approved it for the U.S.

The very fast and short-pulsed (femtosecond) laser creates a thin disc within the cornea, which is removed through a cut on the corneal surface. Removing the tissue changes the shape of the cornea.

The procedure takes 15 to 20 minutes with the laser activated for about 90 seconds per eye. Both eyes can be treated in the same session. With SMILE, unlike LASIK, no tissue is vaporized, meaning wound recovery time is faster, and with no corneal flap created, there is no risk of flap dislocation.

A clinical study found complications were rare, and by month 12 postsurgery there were only 4 reports of moderate or severe glare and 1 of moderate or severe halos. The most commonly reported effects were starbursts, blurred vision, and difficulty judging distance or depth perception, but at 12 months, more patients reported improvement than worsening. At the 6-month follow-up, 287 of 328 patients were seeing 20/20 or better without glasses.

“We are thrilled to extend this treatment option to active duty service members under the Warfighter Refractive Eye Surgery Program,” said U.S. Army Lt. Col. Bruce Rivers, director of the program at Belvoir Hospital.

The surgery will make a difference for patients. One of the first to have it done, Navy Petty Officer 1st Class Christopher Mahmood, a submarine mechanic, said, “On a submarine we have to be able to put our breathing equipment on in approximately 30 seconds, in case of emergency. Glasses make this difficult. Getting this surgery means I have one less thing to worry about while deployed and can focus 100 percent on the mission.”

The SMILE procedure will be available at Belvoir Hospital, San Diego Naval Medical Center, and Wilford Hall in San Antonio.

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.

The ability of serum troponin measurement in the diagnosis of acute myocardial infarction (AMI) was validated in patients with at least a moderate pretest probability for the disease.¹ The diagnostic yield of troponin testing in clinical trials has been between 20% and 50%, excluded patients thought unlikely to have AMI. In practice, physicians often encounter low-risk patients and patients in whom the diagnosis on initial presentation is unclear. Several noncardiac diagnoses, such as pneumonia and respiratory failure, are associated with an elevated troponin level in the absence of AMI, but patients can present with symptoms similar or identical to those of patients who present with AMI.^2-4 Elevated troponin level in sepsis has been associated with worsened prognosis, though there is no evidence that this finding alters management. An American College of Cardiology Foundation opinion published in 2012 expressly recommends against troponin testing in patients with sepsis.⁴

The only guideline-based indication for troponin testing is the diagnosis or exclusion of AMI.⁵ We conducted a comprehensive review of troponin testing in our healthcare system to see whether testing might be used in clinical settings in which AMI was unlikely.

We retrospectively obtained data on all visits to 14 hospitals in an integrated healthcare system in Texas between June 2013 and June 2014. We analyzed data for all hospital encounters during which a troponin assay was ordered and a troponin level reported—including qualitative point-of-care assays and quantitative laboratory troponin I measurements. We identified 93,436 visits. Quantitative measurements were divided into negative (<0.05 ng/mL), indeterminate (0.05-0.09 ng/mL), and elevated (>0.09 ng/mL), based on the reference ranges reported to physicians. We associated troponin levels with ICD-9 (International Classification of Diseases, Ninth Revision) primary and secondary diagnoses, grouping ICD-9 codes 410 (AMI), 411 (other acute or subacute forms of ischemic heart disease [IHD]), 412 (old myocardial infarction), 413 (angina pectoris), and 414 (other forms of chronic IHD) as representing IHD diagnoses.

To further evaluate troponin testing, we constructed 2 contingency matrices (Table).⁶ We included visits for which both primary and secondary diagnoses were available for review and for which quantitative troponin I measurements were available; 92,445 encounters met criteria for inclusion in matrix calculations. In the first matrix (part A of Table), a primary diagnosis of any AMI (ICD-9 code 410) was used as “positive” and all others “negative.” In the second matrix (part B of Table), “positive” includes any primary or secondary diagnosis of AMI.

We identified a total of 93,436 hospital visits associated with troponin testing; 179,239 troponin measurements were associated with these visits (an average of 1.81 per encounter). Of these visits, 59,897 (64.1%) were associated with a single measurement. Of the 179,239 measurements, 147,051 (82.1%) were negative, 21,881 (12.1%) indeterminate, and 10,307 (5.8%) positive. Primary diagnoses of hypertension, dizziness, abdominal pain, anxiety, dehydration, and headache associated with troponin testing comprised 6127 encounters and had no associated elevated troponin levels. Several non-cardiac primary diagnoses were associated with significant numbers of elevated troponin values including septicemia (27%), acute respiratory failure (28%), and cerebrovascular accident (10%). Seventy-six percent of encounters associated with troponin testing had no primary or secondary IHD diagnosis. Only 2% of 16,941 visits with a primary diagnosis of chest pain were associated with abnormal troponin levels (Figure).

Analysis of contingency matrices revealed AMI prevalence of 2.6% when primary AMI diagnoses were considered and 3.5% when any AMI diagnoses were considered. Sensitivity and specificity were high (>90%), and negative predictive value extremely high (>99%) in each circumstance. However, positive predictive values were low (21.7% and 28.8%, respectively), indicating the majority of patients with elevated troponin levels were not reported to have AMI by attending physicians.

We were surprised to find that troponin level was measured only once during 64% of the hospital encounters. Although there are clinical scenarios in which a single measurement might be indicated, detecting a rise or fall in troponin level is integral to the diagnosis of AMI, which is why guidelines recommend serial measurement.⁴ We were also surprised to find a low rate of either primary or secondary AMI in patients tested. As others have found,^2,3 elevated troponin levels were associated with noncardiac primary diagnoses, such as sepsis, respiratory failure, and stroke. Of interest, the majority (72%) of patients with elevated troponin levels did not receive a primary or secondary diagnosis of AMI.

Determining the appropriate level of use for a diagnostic laboratory test can be difficult. Primary diagnostic codes, including codes for headache and dizziness, accounted for thousands of tested patients but were associated with no elevated troponin levels. On the other hand, sepsis, pneumonia, and stroke were associated with high rates of elevated troponin levels. Elevated troponin levels likely precipitate cardiology consultation and testing, which increase cost of care perhaps without improving either quality or value of care. However, evidence for the potential prognostic value of testing has led to ongoing research at our institution to evaluate whether troponin measurement might guide better management of such patients.

Appropriate use criteria have been developed for many diagnostic studies, including echocardiography, stress testing, and cardiac catheterization, but not for laboratory testing. Our data suggest possible overuse of troponin testing in our healthcare system. The low AMI incidence we found (2.6%-3.5%) indicates that many patients without AMI are being tested.

Although it is impossible to accurately estimate sensitivity and specificity of testing post hoc, it is reassuring to see that measured sensitivity, specificity, and negative predictive values were all high and consistent with published values from prospective clinical trials.^7,8

As potential roles for troponin testing develop for patients without primary cardiac disease, it becomes even more important to develop guidelines for testing and to avoid universal testing of all hospitalized patients. The high negative predictive value of troponin testing (99%) is attractive to physicians who want to avoid missing AMI. Electronic order sets allow troponin testing to be included alongside “standard” testing, such as complete blood cell counts and comprehensive metabolic panels, and may contribute to overuse.

The troponin assays used in our healthcare system in 2014 likely will be replaced with high-sensitivity assays currently being used in Europe.^9,10 These high-sensitivity assays can improve sensitivity but cannot be expected to increase positive predictive value or reduce false detection rates. When performed as single measurements, hs troponin has the potential to increase the number of elevated troponins detected that are not associated with AMI.

On the basis of our data, we have initiated a system-wide program to improve performance of troponin testing in our healthcare system. We are working with hospitalists and critical care and emergency department physicians to ensure that serial measurements are being performed and that the correct patients are being tested. Future data collection will help determine the success or failure of these efforts.

Disclosure

Nothing to report.

The ability of serum troponin measurement in the diagnosis of acute myocardial infarction (AMI) was validated in patients with at least a moderate pretest probability for the disease.¹ The diagnostic yield of troponin testing in clinical trials has been between 20% and 50%, excluded patients thought unlikely to have AMI. In practice, physicians often encounter low-risk patients and patients in whom the diagnosis on initial presentation is unclear. Several noncardiac diagnoses, such as pneumonia and respiratory failure, are associated with an elevated troponin level in the absence of AMI, but patients can present with symptoms similar or identical to those of patients who present with AMI.^2-4 Elevated troponin level in sepsis has been associated with worsened prognosis, though there is no evidence that this finding alters management. An American College of Cardiology Foundation opinion published in 2012 expressly recommends against troponin testing in patients with sepsis.⁴

The only guideline-based indication for troponin testing is the diagnosis or exclusion of AMI.⁵ We conducted a comprehensive review of troponin testing in our healthcare system to see whether testing might be used in clinical settings in which AMI was unlikely.

We retrospectively obtained data on all visits to 14 hospitals in an integrated healthcare system in Texas between June 2013 and June 2014. We analyzed data for all hospital encounters during which a troponin assay was ordered and a troponin level reported—including qualitative point-of-care assays and quantitative laboratory troponin I measurements. We identified 93,436 visits. Quantitative measurements were divided into negative (<0.05 ng/mL), indeterminate (0.05-0.09 ng/mL), and elevated (>0.09 ng/mL), based on the reference ranges reported to physicians. We associated troponin levels with ICD-9 (International Classification of Diseases, Ninth Revision) primary and secondary diagnoses, grouping ICD-9 codes 410 (AMI), 411 (other acute or subacute forms of ischemic heart disease [IHD]), 412 (old myocardial infarction), 413 (angina pectoris), and 414 (other forms of chronic IHD) as representing IHD diagnoses.

To further evaluate troponin testing, we constructed 2 contingency matrices (Table).⁶ We included visits for which both primary and secondary diagnoses were available for review and for which quantitative troponin I measurements were available; 92,445 encounters met criteria for inclusion in matrix calculations. In the first matrix (part A of Table), a primary diagnosis of any AMI (ICD-9 code 410) was used as “positive” and all others “negative.” In the second matrix (part B of Table), “positive” includes any primary or secondary diagnosis of AMI.

We identified a total of 93,436 hospital visits associated with troponin testing; 179,239 troponin measurements were associated with these visits (an average of 1.81 per encounter). Of these visits, 59,897 (64.1%) were associated with a single measurement. Of the 179,239 measurements, 147,051 (82.1%) were negative, 21,881 (12.1%) indeterminate, and 10,307 (5.8%) positive. Primary diagnoses of hypertension, dizziness, abdominal pain, anxiety, dehydration, and headache associated with troponin testing comprised 6127 encounters and had no associated elevated troponin levels. Several non-cardiac primary diagnoses were associated with significant numbers of elevated troponin values including septicemia (27%), acute respiratory failure (28%), and cerebrovascular accident (10%). Seventy-six percent of encounters associated with troponin testing had no primary or secondary IHD diagnosis. Only 2% of 16,941 visits with a primary diagnosis of chest pain were associated with abnormal troponin levels (Figure).

Analysis of contingency matrices revealed AMI prevalence of 2.6% when primary AMI diagnoses were considered and 3.5% when any AMI diagnoses were considered. Sensitivity and specificity were high (>90%), and negative predictive value extremely high (>99%) in each circumstance. However, positive predictive values were low (21.7% and 28.8%, respectively), indicating the majority of patients with elevated troponin levels were not reported to have AMI by attending physicians.

We were surprised to find that troponin level was measured only once during 64% of the hospital encounters. Although there are clinical scenarios in which a single measurement might be indicated, detecting a rise or fall in troponin level is integral to the diagnosis of AMI, which is why guidelines recommend serial measurement.⁴ We were also surprised to find a low rate of either primary or secondary AMI in patients tested. As others have found,^2,3 elevated troponin levels were associated with noncardiac primary diagnoses, such as sepsis, respiratory failure, and stroke. Of interest, the majority (72%) of patients with elevated troponin levels did not receive a primary or secondary diagnosis of AMI.

Determining the appropriate level of use for a diagnostic laboratory test can be difficult. Primary diagnostic codes, including codes for headache and dizziness, accounted for thousands of tested patients but were associated with no elevated troponin levels. On the other hand, sepsis, pneumonia, and stroke were associated with high rates of elevated troponin levels. Elevated troponin levels likely precipitate cardiology consultation and testing, which increase cost of care perhaps without improving either quality or value of care. However, evidence for the potential prognostic value of testing has led to ongoing research at our institution to evaluate whether troponin measurement might guide better management of such patients.

Appropriate use criteria have been developed for many diagnostic studies, including echocardiography, stress testing, and cardiac catheterization, but not for laboratory testing. Our data suggest possible overuse of troponin testing in our healthcare system. The low AMI incidence we found (2.6%-3.5%) indicates that many patients without AMI are being tested.

Although it is impossible to accurately estimate sensitivity and specificity of testing post hoc, it is reassuring to see that measured sensitivity, specificity, and negative predictive values were all high and consistent with published values from prospective clinical trials.^7,8

As potential roles for troponin testing develop for patients without primary cardiac disease, it becomes even more important to develop guidelines for testing and to avoid universal testing of all hospitalized patients. The high negative predictive value of troponin testing (99%) is attractive to physicians who want to avoid missing AMI. Electronic order sets allow troponin testing to be included alongside “standard” testing, such as complete blood cell counts and comprehensive metabolic panels, and may contribute to overuse.

The troponin assays used in our healthcare system in 2014 likely will be replaced with high-sensitivity assays currently being used in Europe.^9,10 These high-sensitivity assays can improve sensitivity but cannot be expected to increase positive predictive value or reduce false detection rates. When performed as single measurements, hs troponin has the potential to increase the number of elevated troponins detected that are not associated with AMI.

On the basis of our data, we have initiated a system-wide program to improve performance of troponin testing in our healthcare system. We are working with hospitalists and critical care and emergency department physicians to ensure that serial measurements are being performed and that the correct patients are being tested. Future data collection will help determine the success or failure of these efforts.

Disclosure

Nothing to report.

The ability of serum troponin measurement in the diagnosis of acute myocardial infarction (AMI) was validated in patients with at least a moderate pretest probability for the disease.¹ The diagnostic yield of troponin testing in clinical trials has been between 20% and 50%, excluded patients thought unlikely to have AMI. In practice, physicians often encounter low-risk patients and patients in whom the diagnosis on initial presentation is unclear. Several noncardiac diagnoses, such as pneumonia and respiratory failure, are associated with an elevated troponin level in the absence of AMI, but patients can present with symptoms similar or identical to those of patients who present with AMI.^2-4 Elevated troponin level in sepsis has been associated with worsened prognosis, though there is no evidence that this finding alters management. An American College of Cardiology Foundation opinion published in 2012 expressly recommends against troponin testing in patients with sepsis.⁴

The only guideline-based indication for troponin testing is the diagnosis or exclusion of AMI.⁵ We conducted a comprehensive review of troponin testing in our healthcare system to see whether testing might be used in clinical settings in which AMI was unlikely.

We retrospectively obtained data on all visits to 14 hospitals in an integrated healthcare system in Texas between June 2013 and June 2014. We analyzed data for all hospital encounters during which a troponin assay was ordered and a troponin level reported—including qualitative point-of-care assays and quantitative laboratory troponin I measurements. We identified 93,436 visits. Quantitative measurements were divided into negative (<0.05 ng/mL), indeterminate (0.05-0.09 ng/mL), and elevated (>0.09 ng/mL), based on the reference ranges reported to physicians. We associated troponin levels with ICD-9 (International Classification of Diseases, Ninth Revision) primary and secondary diagnoses, grouping ICD-9 codes 410 (AMI), 411 (other acute or subacute forms of ischemic heart disease [IHD]), 412 (old myocardial infarction), 413 (angina pectoris), and 414 (other forms of chronic IHD) as representing IHD diagnoses.

To further evaluate troponin testing, we constructed 2 contingency matrices (Table).⁶ We included visits for which both primary and secondary diagnoses were available for review and for which quantitative troponin I measurements were available; 92,445 encounters met criteria for inclusion in matrix calculations. In the first matrix (part A of Table), a primary diagnosis of any AMI (ICD-9 code 410) was used as “positive” and all others “negative.” In the second matrix (part B of Table), “positive” includes any primary or secondary diagnosis of AMI.

We identified a total of 93,436 hospital visits associated with troponin testing; 179,239 troponin measurements were associated with these visits (an average of 1.81 per encounter). Of these visits, 59,897 (64.1%) were associated with a single measurement. Of the 179,239 measurements, 147,051 (82.1%) were negative, 21,881 (12.1%) indeterminate, and 10,307 (5.8%) positive. Primary diagnoses of hypertension, dizziness, abdominal pain, anxiety, dehydration, and headache associated with troponin testing comprised 6127 encounters and had no associated elevated troponin levels. Several non-cardiac primary diagnoses were associated with significant numbers of elevated troponin values including septicemia (27%), acute respiratory failure (28%), and cerebrovascular accident (10%). Seventy-six percent of encounters associated with troponin testing had no primary or secondary IHD diagnosis. Only 2% of 16,941 visits with a primary diagnosis of chest pain were associated with abnormal troponin levels (Figure).

Analysis of contingency matrices revealed AMI prevalence of 2.6% when primary AMI diagnoses were considered and 3.5% when any AMI diagnoses were considered. Sensitivity and specificity were high (>90%), and negative predictive value extremely high (>99%) in each circumstance. However, positive predictive values were low (21.7% and 28.8%, respectively), indicating the majority of patients with elevated troponin levels were not reported to have AMI by attending physicians.

We were surprised to find that troponin level was measured only once during 64% of the hospital encounters. Although there are clinical scenarios in which a single measurement might be indicated, detecting a rise or fall in troponin level is integral to the diagnosis of AMI, which is why guidelines recommend serial measurement.⁴ We were also surprised to find a low rate of either primary or secondary AMI in patients tested. As others have found,^2,3 elevated troponin levels were associated with noncardiac primary diagnoses, such as sepsis, respiratory failure, and stroke. Of interest, the majority (72%) of patients with elevated troponin levels did not receive a primary or secondary diagnosis of AMI.

Determining the appropriate level of use for a diagnostic laboratory test can be difficult. Primary diagnostic codes, including codes for headache and dizziness, accounted for thousands of tested patients but were associated with no elevated troponin levels. On the other hand, sepsis, pneumonia, and stroke were associated with high rates of elevated troponin levels. Elevated troponin levels likely precipitate cardiology consultation and testing, which increase cost of care perhaps without improving either quality or value of care. However, evidence for the potential prognostic value of testing has led to ongoing research at our institution to evaluate whether troponin measurement might guide better management of such patients.

Appropriate use criteria have been developed for many diagnostic studies, including echocardiography, stress testing, and cardiac catheterization, but not for laboratory testing. Our data suggest possible overuse of troponin testing in our healthcare system. The low AMI incidence we found (2.6%-3.5%) indicates that many patients without AMI are being tested.

Although it is impossible to accurately estimate sensitivity and specificity of testing post hoc, it is reassuring to see that measured sensitivity, specificity, and negative predictive values were all high and consistent with published values from prospective clinical trials.^7,8

As potential roles for troponin testing develop for patients without primary cardiac disease, it becomes even more important to develop guidelines for testing and to avoid universal testing of all hospitalized patients. The high negative predictive value of troponin testing (99%) is attractive to physicians who want to avoid missing AMI. Electronic order sets allow troponin testing to be included alongside “standard” testing, such as complete blood cell counts and comprehensive metabolic panels, and may contribute to overuse.

The troponin assays used in our healthcare system in 2014 likely will be replaced with high-sensitivity assays currently being used in Europe.^9,10 These high-sensitivity assays can improve sensitivity but cannot be expected to increase positive predictive value or reduce false detection rates. When performed as single measurements, hs troponin has the potential to increase the number of elevated troponins detected that are not associated with AMI.

On the basis of our data, we have initiated a system-wide program to improve performance of troponin testing in our healthcare system. We are working with hospitalists and critical care and emergency department physicians to ensure that serial measurements are being performed and that the correct patients are being tested. Future data collection will help determine the success or failure of these efforts.

Disclosure

Nothing to report.

Electronic consultation (e-consult) in the outpatient setting allows subspecialists to provide assessment and recommendations for patients without in-person visits.¹ An e-consult is an asynchronous communication that uses the electronic medical record (EMR) and typically involves an electronic order from a requesting provider and an electronic note from a consulting provider. The initial motivation for developing this consultation modality was to improve access to subspecialty care for patients in the primary care setting, and findings of studies at several sites support this claim.^1-4 In addition, e-consult may also reduce cost because converting unnecessary face-to-face encounters into e-consults reduces patients’ travel costs and healthcare organizations’ expensive subspecialty clinic time.^3,5 Moreover, instead of addressing less complex clinical questions in informal, undocumented face-to-face or telephone “curbside” consultations with specialists, providers can instead ask for e-consults and thereby ensure thorough chart review and proper documentation.⁶

Use of e-consults in the inpatient setting is relatively novel.⁷ In addition to having the advantages already mentioned, e-consults are faster than in-person bedside consultations and may be beneficial in the fast-moving inpatient care setting. Finally, healthcare systems with multiple hospital sites may not have the capacity to physically locate subspecialists at each site, which makes e-consults attractive for avoiding unnecessary travel time.

In this article, we describe how we developed an inpatient e-consult protocol for a new, remote hospital within our healthcare system and explore data on safety and physician attitudes after e-consult implementation.

The Institutional Review Board of the University of California San Francisco (UCSF) approved this study.

Setting

In February 2015, UCSF opened a new hospital in the Mission Bay neighborhood of San Francisco, 4 miles from the existing hospital. The new hospital is home to several adult inpatient services: urology, otolaryngology, colorectal surgery, obstetrics, and gynecologic surgery. A hospitalist is on-site 24 hours a day to provide consultation for these services around issues that relate to internal medicine. A hospitalist who requires subspecialty expertise to answer a clinical question can request a consultation by in-person visit, video telemedicine, or e-consult, each of which is available 24/7. Almost all of the medicine subspecialists work on the existing campus, not in Mission Bay.

Protocol Development and Implementation

The protocol for the e-consult program was developed over several months by an interdisciplinary group that included 3 hospitalists, 1 obstetrician, 1 project manager, and 1 informaticist. The group outlined the process for requesting and completing an e-consult (Figure), designed a note template for consultants to use for EMR documentation, conducted outreach with subspecialty groups to discuss the protocol, and developed an EMR report to track e-consult use and content over time. As our medical center does not bill payers for inpatient e-consults, e-consult note tracking is used to provide reimbursement internally, from the medical center to the respective departments of the consultants. Reimbursement is made at a set rate per e-consult note, with the rate set to approximate the reimbursement of a low-acuity in-person consult on the main campus.

The workflow of an e-consult is as follows: (1) When a primary team requires a consultation on an issue that falls within the purview of internal medicine, it pages the on-site hospitalist. (2) The hospitalist accepts the consultation by phone, reviews the chart, and examines the patient. (3) If the hospitalist requires subspecialty assistance to answer a clinical question, he or she contacts the appropriate subspecialty service by pager. (4) The subspecialist speaks with the hospitalist about the consultation question, and together they decide if an e-consult is appropriate, based on the complexity of the clinical scenario. (5) The subspecialist reviews the patient’s chart and documents their care plan recommendations in an e-consult note. Consultants can use e-consult for both initial and follow-up assessment, and there is no strict requirement that they also contact the hospitalist or the primary team by phone in addition to consultation note. Given their novelty, almost all e-consults are performed by subspecialist attendings, not residents or fellows.

Evaluation

Each month, we tracked e-consult use using an EMR report built as part of the implementation of the program. For the first four months of implementation, every patient who received an e-consult also had a manual chart review of the period around the e-consult, performed by a hospitalist, in order to audit for any potential safety issues. These issues included, for example, an e-consult performed for a patient whose complexity or severity of illness was felt to be too great to defer an in-person visit, or a patient who received e-consult recommendations that were significantly retracted in a follow-up in-person note.

Eight months after the program started, we assessed experience by electronically surveying the 9 hospitalists and 11 consultants who had requested or performed at least 2 e-consults.⁸ Survey items were measured on a 5-point Likert scale: strongly disagree to strongly agree. The items, which related to ease of calling for a consultation, quality of e-consults, impact on clinical care, safety concerns, and satisfaction, were inspired by themes identified in a systematic review of the literature on e-consults in the outpatient setting.² We sent 2 reminders to responders. Data were summarized using descriptive statistics. Analysis was performed in SPSS version 22.0 (IBM).

There were 143 initial subspecialty consultations by e-consult between program launch in February 2015 and manuscript preparation in February 2016, an average of 11 e-consults per month. There were 313 total e-consult notes (these included both initial and follow-up e-consult notes). By comparison, 240 initial in-person consultations occurred during the same period, and there were 435 total in-person consultation notes (46% new or initial notes, 54% follow-up notes). The top 5 subspecialties by volume of e-consults were infectious disease (35%), hematology (20%), endocrinology (14%), nephrology (13%), and cardiology (8%). For reference, e-consults are also available from psychiatry, neurology, oncology, gastroenterology, pulmonology, and rheumatology. Percentage of consultations performed during daytime hours (defined as 8 a.m. to 5 p.m.) was 92% for e-consults and 96% for in-person consultations.

There were no e-consult–related patient safety issues reported through the medical center’s incident reporting system during the study period. There were also no patient safety issues identified in the manual audits of 80 charts during the first 4 months of the program.

Seven (78%) of 9 hospitalists and 7 (64%) of 11 consultants completed the survey. Both groups agreed that e-consults were easy to use and efficient (Table). All hospitalists were satisfied with the quality of e-consult recommendations, but only 3 (43%) of the 7 consultants agreed they could provide high-quality consultation by e-consult. In their comments, 2 consultants expressed concerns. One concern was about missing crucial information by performing only a chart review, and the other was about being tempted to perform an e-consult simply because it is expedient.

Although use of e-consults in the outpatient setting is relatively commonplace, our program represents a novel use of e-consults in safely and efficiently providing subspecialty consultation to inpatients at a remote hospital.

For hospitalists, an e-consult system offers numerous benefits. Clinical questions beyond an internists’ scope of practice come up often, and simple questions might traditionally result in an informal curbside consult. While a curbside consult provides answers faster than an in-person visit, it creates risks for the requesting hospitalists: the consultants only know what they are told, whether the information is incomplete or erroneous; their opinions are given without documentation or compensation, which reduces a sense of accountability; and the lack of documentation does not allow their advice to persist in the chart as a reference for future providers.⁹ Our e-consult program solves these problems by requiring that consultants perform chart review and provide documentation as well as obligating the medical center to pay a small compensation to consultants for their time. We hope this lowers the bar to requesting consultation for remote sites, where the alternative would be burdensome travel time to do an in-person visit.

In our study, hospitalists were universally pleased with the quality of e-consult recommendations, but only 43% of consultants agreed. These findings correlate with the literature on e-consults in the outpatient setting.² Unfortunately, our survey comments did not shed further light on this sentiment. In the outpatient literature, consultants were most concerned with having a clear clinical question, facing the liability of providing recommendations without performing an examination, and receiving appropriate compensation for answering e-consults.

The generalizability of our program findings is limited most significantly by the particular arrangement of our clinical services: Our remote site is home to a select group of adult inpatient services, a hospitalist is available on-site for these services 24 hours a day, and the distance to the remote site can be overcome with modest effort should a patient require an in-person visit in the initial or follow-up period. The generalizability of our safety findings is limited by the use of a single reviewer for chart auditing.

Given the rise of accountable care organizations and the prevalence of hospital mergers in the healthcare landscape, we believe that healthcare systems that operate remote sites under constrained budgets could look to e-consults to more cost-effectively extend subspecialty expertise across the inpatient enterprise. With improvements in health information exchange, it may also become feasible for consultants to offer e-consults to hospitals outside a medical center’s network. Our study showed that inpatient e-consult programs can be developed and implemented, that they appear not to pose any significant safety issues, and that they can facilitate delivery of timely clinical care.

Acknowledgment

The authors thank Raphaela Levy-Moore for creating and implementing the e-consult note template for our electronic medical record.

Disclosure

Nothing to report.

Electronic consultation (e-consult) in the outpatient setting allows subspecialists to provide assessment and recommendations for patients without in-person visits.¹ An e-consult is an asynchronous communication that uses the electronic medical record (EMR) and typically involves an electronic order from a requesting provider and an electronic note from a consulting provider. The initial motivation for developing this consultation modality was to improve access to subspecialty care for patients in the primary care setting, and findings of studies at several sites support this claim.^1-4 In addition, e-consult may also reduce cost because converting unnecessary face-to-face encounters into e-consults reduces patients’ travel costs and healthcare organizations’ expensive subspecialty clinic time.^3,5 Moreover, instead of addressing less complex clinical questions in informal, undocumented face-to-face or telephone “curbside” consultations with specialists, providers can instead ask for e-consults and thereby ensure thorough chart review and proper documentation.⁶

Use of e-consults in the inpatient setting is relatively novel.⁷ In addition to having the advantages already mentioned, e-consults are faster than in-person bedside consultations and may be beneficial in the fast-moving inpatient care setting. Finally, healthcare systems with multiple hospital sites may not have the capacity to physically locate subspecialists at each site, which makes e-consults attractive for avoiding unnecessary travel time.

In this article, we describe how we developed an inpatient e-consult protocol for a new, remote hospital within our healthcare system and explore data on safety and physician attitudes after e-consult implementation.

The Institutional Review Board of the University of California San Francisco (UCSF) approved this study.

Setting

In February 2015, UCSF opened a new hospital in the Mission Bay neighborhood of San Francisco, 4 miles from the existing hospital. The new hospital is home to several adult inpatient services: urology, otolaryngology, colorectal surgery, obstetrics, and gynecologic surgery. A hospitalist is on-site 24 hours a day to provide consultation for these services around issues that relate to internal medicine. A hospitalist who requires subspecialty expertise to answer a clinical question can request a consultation by in-person visit, video telemedicine, or e-consult, each of which is available 24/7. Almost all of the medicine subspecialists work on the existing campus, not in Mission Bay.

Protocol Development and Implementation

The protocol for the e-consult program was developed over several months by an interdisciplinary group that included 3 hospitalists, 1 obstetrician, 1 project manager, and 1 informaticist. The group outlined the process for requesting and completing an e-consult (Figure), designed a note template for consultants to use for EMR documentation, conducted outreach with subspecialty groups to discuss the protocol, and developed an EMR report to track e-consult use and content over time. As our medical center does not bill payers for inpatient e-consults, e-consult note tracking is used to provide reimbursement internally, from the medical center to the respective departments of the consultants. Reimbursement is made at a set rate per e-consult note, with the rate set to approximate the reimbursement of a low-acuity in-person consult on the main campus.

The workflow of an e-consult is as follows: (1) When a primary team requires a consultation on an issue that falls within the purview of internal medicine, it pages the on-site hospitalist. (2) The hospitalist accepts the consultation by phone, reviews the chart, and examines the patient. (3) If the hospitalist requires subspecialty assistance to answer a clinical question, he or she contacts the appropriate subspecialty service by pager. (4) The subspecialist speaks with the hospitalist about the consultation question, and together they decide if an e-consult is appropriate, based on the complexity of the clinical scenario. (5) The subspecialist reviews the patient’s chart and documents their care plan recommendations in an e-consult note. Consultants can use e-consult for both initial and follow-up assessment, and there is no strict requirement that they also contact the hospitalist or the primary team by phone in addition to consultation note. Given their novelty, almost all e-consults are performed by subspecialist attendings, not residents or fellows.

Evaluation

Each month, we tracked e-consult use using an EMR report built as part of the implementation of the program. For the first four months of implementation, every patient who received an e-consult also had a manual chart review of the period around the e-consult, performed by a hospitalist, in order to audit for any potential safety issues. These issues included, for example, an e-consult performed for a patient whose complexity or severity of illness was felt to be too great to defer an in-person visit, or a patient who received e-consult recommendations that were significantly retracted in a follow-up in-person note.

Eight months after the program started, we assessed experience by electronically surveying the 9 hospitalists and 11 consultants who had requested or performed at least 2 e-consults.⁸ Survey items were measured on a 5-point Likert scale: strongly disagree to strongly agree. The items, which related to ease of calling for a consultation, quality of e-consults, impact on clinical care, safety concerns, and satisfaction, were inspired by themes identified in a systematic review of the literature on e-consults in the outpatient setting.² We sent 2 reminders to responders. Data were summarized using descriptive statistics. Analysis was performed in SPSS version 22.0 (IBM).

There were 143 initial subspecialty consultations by e-consult between program launch in February 2015 and manuscript preparation in February 2016, an average of 11 e-consults per month. There were 313 total e-consult notes (these included both initial and follow-up e-consult notes). By comparison, 240 initial in-person consultations occurred during the same period, and there were 435 total in-person consultation notes (46% new or initial notes, 54% follow-up notes). The top 5 subspecialties by volume of e-consults were infectious disease (35%), hematology (20%), endocrinology (14%), nephrology (13%), and cardiology (8%). For reference, e-consults are also available from psychiatry, neurology, oncology, gastroenterology, pulmonology, and rheumatology. Percentage of consultations performed during daytime hours (defined as 8 a.m. to 5 p.m.) was 92% for e-consults and 96% for in-person consultations.

There were no e-consult–related patient safety issues reported through the medical center’s incident reporting system during the study period. There were also no patient safety issues identified in the manual audits of 80 charts during the first 4 months of the program.

Seven (78%) of 9 hospitalists and 7 (64%) of 11 consultants completed the survey. Both groups agreed that e-consults were easy to use and efficient (Table). All hospitalists were satisfied with the quality of e-consult recommendations, but only 3 (43%) of the 7 consultants agreed they could provide high-quality consultation by e-consult. In their comments, 2 consultants expressed concerns. One concern was about missing crucial information by performing only a chart review, and the other was about being tempted to perform an e-consult simply because it is expedient.

Although use of e-consults in the outpatient setting is relatively commonplace, our program represents a novel use of e-consults in safely and efficiently providing subspecialty consultation to inpatients at a remote hospital.

For hospitalists, an e-consult system offers numerous benefits. Clinical questions beyond an internists’ scope of practice come up often, and simple questions might traditionally result in an informal curbside consult. While a curbside consult provides answers faster than an in-person visit, it creates risks for the requesting hospitalists: the consultants only know what they are told, whether the information is incomplete or erroneous; their opinions are given without documentation or compensation, which reduces a sense of accountability; and the lack of documentation does not allow their advice to persist in the chart as a reference for future providers.⁹ Our e-consult program solves these problems by requiring that consultants perform chart review and provide documentation as well as obligating the medical center to pay a small compensation to consultants for their time. We hope this lowers the bar to requesting consultation for remote sites, where the alternative would be burdensome travel time to do an in-person visit.

In our study, hospitalists were universally pleased with the quality of e-consult recommendations, but only 43% of consultants agreed. These findings correlate with the literature on e-consults in the outpatient setting.² Unfortunately, our survey comments did not shed further light on this sentiment. In the outpatient literature, consultants were most concerned with having a clear clinical question, facing the liability of providing recommendations without performing an examination, and receiving appropriate compensation for answering e-consults.

The generalizability of our program findings is limited most significantly by the particular arrangement of our clinical services: Our remote site is home to a select group of adult inpatient services, a hospitalist is available on-site for these services 24 hours a day, and the distance to the remote site can be overcome with modest effort should a patient require an in-person visit in the initial or follow-up period. The generalizability of our safety findings is limited by the use of a single reviewer for chart auditing.

Given the rise of accountable care organizations and the prevalence of hospital mergers in the healthcare landscape, we believe that healthcare systems that operate remote sites under constrained budgets could look to e-consults to more cost-effectively extend subspecialty expertise across the inpatient enterprise. With improvements in health information exchange, it may also become feasible for consultants to offer e-consults to hospitals outside a medical center’s network. Our study showed that inpatient e-consult programs can be developed and implemented, that they appear not to pose any significant safety issues, and that they can facilitate delivery of timely clinical care.

Acknowledgment

The authors thank Raphaela Levy-Moore for creating and implementing the e-consult note template for our electronic medical record.

Disclosure

Nothing to report.

Electronic consultation (e-consult) in the outpatient setting allows subspecialists to provide assessment and recommendations for patients without in-person visits.¹ An e-consult is an asynchronous communication that uses the electronic medical record (EMR) and typically involves an electronic order from a requesting provider and an electronic note from a consulting provider. The initial motivation for developing this consultation modality was to improve access to subspecialty care for patients in the primary care setting, and findings of studies at several sites support this claim.^1-4 In addition, e-consult may also reduce cost because converting unnecessary face-to-face encounters into e-consults reduces patients’ travel costs and healthcare organizations’ expensive subspecialty clinic time.^3,5 Moreover, instead of addressing less complex clinical questions in informal, undocumented face-to-face or telephone “curbside” consultations with specialists, providers can instead ask for e-consults and thereby ensure thorough chart review and proper documentation.⁶

Use of e-consults in the inpatient setting is relatively novel.⁷ In addition to having the advantages already mentioned, e-consults are faster than in-person bedside consultations and may be beneficial in the fast-moving inpatient care setting. Finally, healthcare systems with multiple hospital sites may not have the capacity to physically locate subspecialists at each site, which makes e-consults attractive for avoiding unnecessary travel time.

In this article, we describe how we developed an inpatient e-consult protocol for a new, remote hospital within our healthcare system and explore data on safety and physician attitudes after e-consult implementation.

The Institutional Review Board of the University of California San Francisco (UCSF) approved this study.

Setting

In February 2015, UCSF opened a new hospital in the Mission Bay neighborhood of San Francisco, 4 miles from the existing hospital. The new hospital is home to several adult inpatient services: urology, otolaryngology, colorectal surgery, obstetrics, and gynecologic surgery. A hospitalist is on-site 24 hours a day to provide consultation for these services around issues that relate to internal medicine. A hospitalist who requires subspecialty expertise to answer a clinical question can request a consultation by in-person visit, video telemedicine, or e-consult, each of which is available 24/7. Almost all of the medicine subspecialists work on the existing campus, not in Mission Bay.

Protocol Development and Implementation

The protocol for the e-consult program was developed over several months by an interdisciplinary group that included 3 hospitalists, 1 obstetrician, 1 project manager, and 1 informaticist. The group outlined the process for requesting and completing an e-consult (Figure), designed a note template for consultants to use for EMR documentation, conducted outreach with subspecialty groups to discuss the protocol, and developed an EMR report to track e-consult use and content over time. As our medical center does not bill payers for inpatient e-consults, e-consult note tracking is used to provide reimbursement internally, from the medical center to the respective departments of the consultants. Reimbursement is made at a set rate per e-consult note, with the rate set to approximate the reimbursement of a low-acuity in-person consult on the main campus.

The workflow of an e-consult is as follows: (1) When a primary team requires a consultation on an issue that falls within the purview of internal medicine, it pages the on-site hospitalist. (2) The hospitalist accepts the consultation by phone, reviews the chart, and examines the patient. (3) If the hospitalist requires subspecialty assistance to answer a clinical question, he or she contacts the appropriate subspecialty service by pager. (4) The subspecialist speaks with the hospitalist about the consultation question, and together they decide if an e-consult is appropriate, based on the complexity of the clinical scenario. (5) The subspecialist reviews the patient’s chart and documents their care plan recommendations in an e-consult note. Consultants can use e-consult for both initial and follow-up assessment, and there is no strict requirement that they also contact the hospitalist or the primary team by phone in addition to consultation note. Given their novelty, almost all e-consults are performed by subspecialist attendings, not residents or fellows.

Evaluation

Each month, we tracked e-consult use using an EMR report built as part of the implementation of the program. For the first four months of implementation, every patient who received an e-consult also had a manual chart review of the period around the e-consult, performed by a hospitalist, in order to audit for any potential safety issues. These issues included, for example, an e-consult performed for a patient whose complexity or severity of illness was felt to be too great to defer an in-person visit, or a patient who received e-consult recommendations that were significantly retracted in a follow-up in-person note.

Eight months after the program started, we assessed experience by electronically surveying the 9 hospitalists and 11 consultants who had requested or performed at least 2 e-consults.⁸ Survey items were measured on a 5-point Likert scale: strongly disagree to strongly agree. The items, which related to ease of calling for a consultation, quality of e-consults, impact on clinical care, safety concerns, and satisfaction, were inspired by themes identified in a systematic review of the literature on e-consults in the outpatient setting.² We sent 2 reminders to responders. Data were summarized using descriptive statistics. Analysis was performed in SPSS version 22.0 (IBM).

There were 143 initial subspecialty consultations by e-consult between program launch in February 2015 and manuscript preparation in February 2016, an average of 11 e-consults per month. There were 313 total e-consult notes (these included both initial and follow-up e-consult notes). By comparison, 240 initial in-person consultations occurred during the same period, and there were 435 total in-person consultation notes (46% new or initial notes, 54% follow-up notes). The top 5 subspecialties by volume of e-consults were infectious disease (35%), hematology (20%), endocrinology (14%), nephrology (13%), and cardiology (8%). For reference, e-consults are also available from psychiatry, neurology, oncology, gastroenterology, pulmonology, and rheumatology. Percentage of consultations performed during daytime hours (defined as 8 a.m. to 5 p.m.) was 92% for e-consults and 96% for in-person consultations.

There were no e-consult–related patient safety issues reported through the medical center’s incident reporting system during the study period. There were also no patient safety issues identified in the manual audits of 80 charts during the first 4 months of the program.

Seven (78%) of 9 hospitalists and 7 (64%) of 11 consultants completed the survey. Both groups agreed that e-consults were easy to use and efficient (Table). All hospitalists were satisfied with the quality of e-consult recommendations, but only 3 (43%) of the 7 consultants agreed they could provide high-quality consultation by e-consult. In their comments, 2 consultants expressed concerns. One concern was about missing crucial information by performing only a chart review, and the other was about being tempted to perform an e-consult simply because it is expedient.

Although use of e-consults in the outpatient setting is relatively commonplace, our program represents a novel use of e-consults in safely and efficiently providing subspecialty consultation to inpatients at a remote hospital.

For hospitalists, an e-consult system offers numerous benefits. Clinical questions beyond an internists’ scope of practice come up often, and simple questions might traditionally result in an informal curbside consult. While a curbside consult provides answers faster than an in-person visit, it creates risks for the requesting hospitalists: the consultants only know what they are told, whether the information is incomplete or erroneous; their opinions are given without documentation or compensation, which reduces a sense of accountability; and the lack of documentation does not allow their advice to persist in the chart as a reference for future providers.⁹ Our e-consult program solves these problems by requiring that consultants perform chart review and provide documentation as well as obligating the medical center to pay a small compensation to consultants for their time. We hope this lowers the bar to requesting consultation for remote sites, where the alternative would be burdensome travel time to do an in-person visit.

In our study, hospitalists were universally pleased with the quality of e-consult recommendations, but only 43% of consultants agreed. These findings correlate with the literature on e-consults in the outpatient setting.² Unfortunately, our survey comments did not shed further light on this sentiment. In the outpatient literature, consultants were most concerned with having a clear clinical question, facing the liability of providing recommendations without performing an examination, and receiving appropriate compensation for answering e-consults.

The generalizability of our program findings is limited most significantly by the particular arrangement of our clinical services: Our remote site is home to a select group of adult inpatient services, a hospitalist is available on-site for these services 24 hours a day, and the distance to the remote site can be overcome with modest effort should a patient require an in-person visit in the initial or follow-up period. The generalizability of our safety findings is limited by the use of a single reviewer for chart auditing.

Given the rise of accountable care organizations and the prevalence of hospital mergers in the healthcare landscape, we believe that healthcare systems that operate remote sites under constrained budgets could look to e-consults to more cost-effectively extend subspecialty expertise across the inpatient enterprise. With improvements in health information exchange, it may also become feasible for consultants to offer e-consults to hospitals outside a medical center’s network. Our study showed that inpatient e-consult programs can be developed and implemented, that they appear not to pose any significant safety issues, and that they can facilitate delivery of timely clinical care.

Acknowledgment

The authors thank Raphaela Levy-Moore for creating and implementing the e-consult note template for our electronic medical record.

Disclosure

Nothing to report.

Avoiding repeated complete blood count (CBC) tests in the face of clinical and lab stability is a focus of the Choosing Wisely^® initiatives launched by the American Board of Internal Medicine Foundation¹ and endorsed by the Society of Hospital Medicine.² However, specific scenarios in which daily morning labs can be safely avoided have not been identified. The goal of this study was to identify situations in which routine CBC testing can be avoided in patients with community-acquired pneumonia (CAP), one of the most common reasons for hospital admission.³

This was a retrospective study of 50 patients with CAP discharged from our hospital between February 1, 2015 and May 1, 2015. We performed chart abstractions collecting daily vital signs, lab results, provider notes including assessments and plans (A&Ps), and order entry logs, as well as documentation indicating whether a lab result or clinical finding appeared to affect clinical management (eg, a new order or documentation of changing plans). Both escalations and de-escalations were included as management changes. For example, if the note stated “Persistent leukocytosis, add vancomycin,” then the clinical action of expanded antibiotic coverage would be attributed to the CBC.

We defined clinical stability based on Definition B of the Pneumonia Patient Outcomes Research Team (PORT) study criteria.⁴ We used descriptive statistics and likelihood ratios to characterize the utility of CBC testing in terms of producing clinical management changes. Likelihood ratios were calculated with the “test” representing a CBC being ordered or not ordered and the outcome being any change in management independent of whether it was due to the CBC.

Of 50 patients, 33 (66%) were female, the mean age was 75 years, the mean length of stay was 2.8 days, and the median CURB-65 score,⁵ an estimate of mortality in CAP used for decision-making about inpatient versus outpatient treatment, was 1 (25^th to 75^th interquartile range: 1, 2); no patients had a CURB score greater than 3 (Table 1). Forty-one (82%) patients met PORT clinical stability criteria prior to discharge, and 30 (75% of stable patients) had CBCs obtained.

On days after admission, 94 subsequent CBCs were obtained. Of these CBCs, 6 (6.4%) were associated with management changes indicated in documentation or orders (Table 2). In 2 of the 6 patients, management changes were likely relevant to pneumonia. In the first case, the patient had a white blood cell count (WBC) of 15.4 on the planned day of discharge but no accompanying clinical changes. Her discharge was potentially delayed pending a repeat CBC which again showed a WBC 14.7; the patient was then discharged without any additional changes in plan. In the second case, the patient experienced new-onset altered mental status on hospital day 3 and increasing O₂ requirement with a rising WBC noted on hospital day 4. Repeat chest x-ray, repeat blood cultures, and an ultrasound for parapneumonic effusion were obtained, and the patient’s symptoms and signs resolved over a period of days without changes in treatment. In the 4 other cases, available documentation suggested the hemoglobin abnormalities found represented chronic or incidental illnesses, specifically iron deficiency anemia, iatrogenic anemia due to fluid resuscitation and hemodilution, previously known chronic lymphocytic leukemia, and thrombocytopenia due to acute infection. In all 6 instances, CBC values improved without treatment intervention.

Though small, our initial study suggests the potential opportunity for savings if Choosing Wisely^® recommendations for CBC testing were implemented in patients with community-acquired pneumonia.

Our study has several limitations. Note-writing practices and ordering patterns likely varied between providers, and documentation bias may play a role in our results. However, we defined whether a CBC was associated with changes in clinical decision-making or management by incorporating a number of mutually reinforcing elements of the medical record. We recognize, however, that our approach may not capture undocumented clinical issues or other cognitive (eg, reassurance of clinical resolution) reasons why CBCs were obtained.

Even with these limitations, the likelihood of a CBC value meaningfully changing clinical management among patients with CAP appears to be quite low as evidenced by the case descriptions, particularly when obtained in stable patients by PORT criteria and on the day of discharge. Whether clinical stability as measured by PORT score can be used to target patients in whom CBC testing is unnecessary is difficult to discern from our data, as the overall utility of CBCs obtained after admission was quite low and the rate of changes in management was also low. However, even if CBCs are not particularly costly, unnecessary testing may produce harm in the form of prolonged length of stay, making even one unnecessary CBC potentially extremely expensive. More research involving larger-scale studies are needed to determine the “number needed to screen” for the daily CBC in CAP to determine if the cost savings from overtesting and treatment outweigh the potential benefit of a single CBC that changes management.

Disclosure

Nothing to report.

Avoiding repeated complete blood count (CBC) tests in the face of clinical and lab stability is a focus of the Choosing Wisely^® initiatives launched by the American Board of Internal Medicine Foundation¹ and endorsed by the Society of Hospital Medicine.² However, specific scenarios in which daily morning labs can be safely avoided have not been identified. The goal of this study was to identify situations in which routine CBC testing can be avoided in patients with community-acquired pneumonia (CAP), one of the most common reasons for hospital admission.³

This was a retrospective study of 50 patients with CAP discharged from our hospital between February 1, 2015 and May 1, 2015. We performed chart abstractions collecting daily vital signs, lab results, provider notes including assessments and plans (A&Ps), and order entry logs, as well as documentation indicating whether a lab result or clinical finding appeared to affect clinical management (eg, a new order or documentation of changing plans). Both escalations and de-escalations were included as management changes. For example, if the note stated “Persistent leukocytosis, add vancomycin,” then the clinical action of expanded antibiotic coverage would be attributed to the CBC.

We defined clinical stability based on Definition B of the Pneumonia Patient Outcomes Research Team (PORT) study criteria.⁴ We used descriptive statistics and likelihood ratios to characterize the utility of CBC testing in terms of producing clinical management changes. Likelihood ratios were calculated with the “test” representing a CBC being ordered or not ordered and the outcome being any change in management independent of whether it was due to the CBC.

Of 50 patients, 33 (66%) were female, the mean age was 75 years, the mean length of stay was 2.8 days, and the median CURB-65 score,⁵ an estimate of mortality in CAP used for decision-making about inpatient versus outpatient treatment, was 1 (25^th to 75^th interquartile range: 1, 2); no patients had a CURB score greater than 3 (Table 1). Forty-one (82%) patients met PORT clinical stability criteria prior to discharge, and 30 (75% of stable patients) had CBCs obtained.

On days after admission, 94 subsequent CBCs were obtained. Of these CBCs, 6 (6.4%) were associated with management changes indicated in documentation or orders (Table 2). In 2 of the 6 patients, management changes were likely relevant to pneumonia. In the first case, the patient had a white blood cell count (WBC) of 15.4 on the planned day of discharge but no accompanying clinical changes. Her discharge was potentially delayed pending a repeat CBC which again showed a WBC 14.7; the patient was then discharged without any additional changes in plan. In the second case, the patient experienced new-onset altered mental status on hospital day 3 and increasing O₂ requirement with a rising WBC noted on hospital day 4. Repeat chest x-ray, repeat blood cultures, and an ultrasound for parapneumonic effusion were obtained, and the patient’s symptoms and signs resolved over a period of days without changes in treatment. In the 4 other cases, available documentation suggested the hemoglobin abnormalities found represented chronic or incidental illnesses, specifically iron deficiency anemia, iatrogenic anemia due to fluid resuscitation and hemodilution, previously known chronic lymphocytic leukemia, and thrombocytopenia due to acute infection. In all 6 instances, CBC values improved without treatment intervention.

Though small, our initial study suggests the potential opportunity for savings if Choosing Wisely^® recommendations for CBC testing were implemented in patients with community-acquired pneumonia.

Our study has several limitations. Note-writing practices and ordering patterns likely varied between providers, and documentation bias may play a role in our results. However, we defined whether a CBC was associated with changes in clinical decision-making or management by incorporating a number of mutually reinforcing elements of the medical record. We recognize, however, that our approach may not capture undocumented clinical issues or other cognitive (eg, reassurance of clinical resolution) reasons why CBCs were obtained.

Even with these limitations, the likelihood of a CBC value meaningfully changing clinical management among patients with CAP appears to be quite low as evidenced by the case descriptions, particularly when obtained in stable patients by PORT criteria and on the day of discharge. Whether clinical stability as measured by PORT score can be used to target patients in whom CBC testing is unnecessary is difficult to discern from our data, as the overall utility of CBCs obtained after admission was quite low and the rate of changes in management was also low. However, even if CBCs are not particularly costly, unnecessary testing may produce harm in the form of prolonged length of stay, making even one unnecessary CBC potentially extremely expensive. More research involving larger-scale studies are needed to determine the “number needed to screen” for the daily CBC in CAP to determine if the cost savings from overtesting and treatment outweigh the potential benefit of a single CBC that changes management.

Disclosure

Nothing to report.

Avoiding repeated complete blood count (CBC) tests in the face of clinical and lab stability is a focus of the Choosing Wisely^® initiatives launched by the American Board of Internal Medicine Foundation¹ and endorsed by the Society of Hospital Medicine.² However, specific scenarios in which daily morning labs can be safely avoided have not been identified. The goal of this study was to identify situations in which routine CBC testing can be avoided in patients with community-acquired pneumonia (CAP), one of the most common reasons for hospital admission.³

This was a retrospective study of 50 patients with CAP discharged from our hospital between February 1, 2015 and May 1, 2015. We performed chart abstractions collecting daily vital signs, lab results, provider notes including assessments and plans (A&Ps), and order entry logs, as well as documentation indicating whether a lab result or clinical finding appeared to affect clinical management (eg, a new order or documentation of changing plans). Both escalations and de-escalations were included as management changes. For example, if the note stated “Persistent leukocytosis, add vancomycin,” then the clinical action of expanded antibiotic coverage would be attributed to the CBC.

We defined clinical stability based on Definition B of the Pneumonia Patient Outcomes Research Team (PORT) study criteria.⁴ We used descriptive statistics and likelihood ratios to characterize the utility of CBC testing in terms of producing clinical management changes. Likelihood ratios were calculated with the “test” representing a CBC being ordered or not ordered and the outcome being any change in management independent of whether it was due to the CBC.

Of 50 patients, 33 (66%) were female, the mean age was 75 years, the mean length of stay was 2.8 days, and the median CURB-65 score,⁵ an estimate of mortality in CAP used for decision-making about inpatient versus outpatient treatment, was 1 (25^th to 75^th interquartile range: 1, 2); no patients had a CURB score greater than 3 (Table 1). Forty-one (82%) patients met PORT clinical stability criteria prior to discharge, and 30 (75% of stable patients) had CBCs obtained.

On days after admission, 94 subsequent CBCs were obtained. Of these CBCs, 6 (6.4%) were associated with management changes indicated in documentation or orders (Table 2). In 2 of the 6 patients, management changes were likely relevant to pneumonia. In the first case, the patient had a white blood cell count (WBC) of 15.4 on the planned day of discharge but no accompanying clinical changes. Her discharge was potentially delayed pending a repeat CBC which again showed a WBC 14.7; the patient was then discharged without any additional changes in plan. In the second case, the patient experienced new-onset altered mental status on hospital day 3 and increasing O₂ requirement with a rising WBC noted on hospital day 4. Repeat chest x-ray, repeat blood cultures, and an ultrasound for parapneumonic effusion were obtained, and the patient’s symptoms and signs resolved over a period of days without changes in treatment. In the 4 other cases, available documentation suggested the hemoglobin abnormalities found represented chronic or incidental illnesses, specifically iron deficiency anemia, iatrogenic anemia due to fluid resuscitation and hemodilution, previously known chronic lymphocytic leukemia, and thrombocytopenia due to acute infection. In all 6 instances, CBC values improved without treatment intervention.

Though small, our initial study suggests the potential opportunity for savings if Choosing Wisely^® recommendations for CBC testing were implemented in patients with community-acquired pneumonia.

Our study has several limitations. Note-writing practices and ordering patterns likely varied between providers, and documentation bias may play a role in our results. However, we defined whether a CBC was associated with changes in clinical decision-making or management by incorporating a number of mutually reinforcing elements of the medical record. We recognize, however, that our approach may not capture undocumented clinical issues or other cognitive (eg, reassurance of clinical resolution) reasons why CBCs were obtained.

Even with these limitations, the likelihood of a CBC value meaningfully changing clinical management among patients with CAP appears to be quite low as evidenced by the case descriptions, particularly when obtained in stable patients by PORT criteria and on the day of discharge. Whether clinical stability as measured by PORT score can be used to target patients in whom CBC testing is unnecessary is difficult to discern from our data, as the overall utility of CBCs obtained after admission was quite low and the rate of changes in management was also low. However, even if CBCs are not particularly costly, unnecessary testing may produce harm in the form of prolonged length of stay, making even one unnecessary CBC potentially extremely expensive. More research involving larger-scale studies are needed to determine the “number needed to screen” for the daily CBC in CAP to determine if the cost savings from overtesting and treatment outweigh the potential benefit of a single CBC that changes management.

Disclosure

Nothing to report.

Addiction is a national epidemic that represents both a pressing need and a significant burden to the healthcare system.¹ Hospitals are increasingly filled with people admitted for medical complications of substance use disorders (SUD).²People with SUD have longer lengths of stay (LOS) and high readmission rates.³ Hospitalization often does not address the root cause—the SUD. For example, many hospitals replace heart valves and deliver prolonged courses of intravenous (IV) antibiotics for endocarditis from injection drug use but do not offer addiction medicine consultation, medication for addiction treatment (MAT), or linkage to posthospital SUD treatment.^4,5

Hospitalization can provide reachable moments for initiating addiction care.⁶ Medications for opioid⁷ and alcohol use disorders⁸ can be started during hospitalization, promoting engagement in outpatient SUD care⁷ and increased uptake of MAT,^7-9 and reducing readmissions.^8,10 Yet, medications for SUD are underprescribed,^11,12 and most hospitals lack inpatient addiction medicine services and pathways to timely SUD care after discharge. Furthermore, traditional SUD treatment programs are often not equipped to manage medically complex patients or they have long waitlists.¹³ Most behavioral-physical health integration occurs in ambulatory settings. This fails to engage patients who do not access primary care. There is an urgent need for models that can improve care for hospitalized patients with SUD.

Here, we describe our experience using patient needs assessment to engage stakeholders and drive local systems change. We also describe the resulting care model, the Improving Addiction Care Team (IMPACT). Our experience provides a potentially useful example to other hospitals and communities seeking to address the national SUD epidemic.

Setting

In 2012, Oregon transformed its Medicaid system by establishing 16 regional “coordinated care organizations” (CCOs) to improve outcomes and slow healthcare spending.¹⁴ In a CCO environment, hospitals assume increased financial risk, yet reforms have focused on the outpatient setting. Therefore, executive leadership at Oregon Health & Science University (OHSU), an urban academic medical center, asked clinician-leaders to design point-of-care improvements for Medicaid-funded adults and build on existing models to improve care for socioeconomically vulnerable adults.^15,16 One priority that emerged was to make improvements for hospitalized adults with SUD. Of the adult inpatients at OHSU, 30% have Medicaid and 15% have SUD by administrative data alone. Before we started our work, OHSU lacked inpatient addiction medicine services.

Local Needs Assessment

To understand local needs and opportunities, we surveyed hospitalized adults with SUD. We used the electronic health record to generate a list of inpatients flagged by nurses for risky alcohol or drug use. A research assistant screened consecutive adults (≥18 years old) and invited those who screened positive for alcohol use (Alcohol Use Disorders Identification Test–Consumption [AUDIT-C])¹⁷ or drug use (single-item screener)¹⁸ to participate. We excluded non-English speakers, incarcerated adults, people using only marijuana or tobacco, psychiatry inpatients, and people unable to consent. Surveys assessed social and demographic factors, healthcare utilization, substance use severity, and treatment experience. Participants who reported high-risk illicit drug or alcohol use¹⁹ were asked to indicate their readiness to change on a 3-point scale developed for this study. Response range included: no interest, interest in cutting back, or interest in quitting. A subset of participants completed in-depth qualitative interviews exploring patient perceptions of substance use treatment needs.²⁰ We obtained hospital administrative data from hospital financial services.

Partner Engagement

We identified community partners with which we had an individual or organizational relationship and a common interest and potential for collaboration. All invited partners agreed to attend initial meetings. We convened leadership and frontline staff across partners. OHSU staff included hospital nursing and social work leaders; infectious disease, hospitalist, and addiction physicians; and health services researchers. Community organizations included Central City Concern (CCC), a community organization serving people facing homelessness and addiction; CODA, Inc., a nonprofit SUD treatment agency; and Coram/CVS infusion pharmacy.

Collectively, we reviewed needs assessment findings and examples from the literature^7-9 to develop strategies to address patient and system needs. We used patient narratives to foster alignment and prioritized areas in which integration could improve quality and costs. We assumed we would petition OHSU and/or Medicaid CCOs to finance efforts and saved potentially challenging budget discussions for later, when partnerships would be more developed. Our task force attended more than 3 large-group meetings and numerous small-group meetings to develop IMPACT.

Needs Assessment

Between September 2014 and April 2015, a research assistant approached 326 patients. Of these, 235 (72%) met study inclusion criteria, and 185 (78%) agreed to participate (Table 1). Of people who reported any substance use within the preceding 3 months, 58% of alcohol users and 67% of drug users said they were interested in cutting back or quitting. Fifty-four percent of participants with moderate- to high-risk opioid use and 16% with moderate- to high-risk alcohol use reported strong interest in MAT. In qualitative interviews, participants described inadequately treated withdrawal, the importance of trust and choice, and long wait times as a barriers to entering treatment after hospital discharge.²⁰

Administrative data revealed high rates of hospital readmissions and longer than expected LOS (Figure). Mean LOS was 10.26 days—4 days more than medicine patients’. Mean LOS was high among participants who required long-term IV antibiotics, particularly those with endocarditis or osteomyelitis (21.75 days; range, 1.00-51.00 days). We excluded one outlier with a 116-day hospitalization.

Intervention Design

Mapping needs to intervention components. We mapped needs assessment findings to 3 main IMPACT components: inpatient addiction medicine consultation service, pathways to posthospital SUD treatment, and medically enhanced residential treatment (MERT) (Table 2).

Inpatient addiction medicine consultation service. We developed this service to address patients’ report of high readiness to change and interest in starting MAT in the hospital. Community partners highlighted the need for peers to increase engagement and trust. Therefore, we included a physician, a social worker, and two peers on our team. The inpatient service engages patients, advises on withdrawal and pain, performs SUD assessments, initiates MAT, and provides counseling and treatment.

Funding the Intervention

We used administrative data to estimate potential savings and tailored a business case to CCO and hospital payers. The CCO business case centered on hospitalization as an opportunity to engage out-of-treatment adults and potentially reduce high-cost readmissions by managing physical and behavioral health needs. Working within budgeting time lines, we used data from the first 165 participants. These participants had 137 readmissions over a mean observation period of 4.5 months. Mean charge per readmission was $31,157 (range, $699-$206,596) and was highest for people with endocarditis (mean, $55,493; range, $23,204-$145,066) and osteomyelitis (mean, $68,774; range, $29,359-$124,481). We estimated that a 10% reduction in 6-month readmissions could avoid $674,863 in charges.

For the hospital, the primary financial incentive was reduced LOS. Given the possibility of shortening hospitalization through MERT, we estimated a 20% mean LOS reduction; for budgeting, we estimated a conservative 10% reduction. A 10% mean LOS reduction would free 205 bed-days (10% × 10.26 days mean LOS × 200 patients) and create space for another 32 inpatient admissions in year 1, assuming no change from medical patients’ 6.26 days mean LOS. The future of bundled payments further bolstered our business case, as did the potential to improve care quality, reduce nonproductive staff time, and increase institutional learning about SUD. Overall program costs approximated projected savings, and the hospital and a local CCO agreed to equally share the costs of the intervention (Table 2).

We have described an innovative approach to developing an SUD intervention for hospitalized adults. Using a process of broad stakeholder engagement, data-driven understanding of population needs, and analysis of financial incentives, we built consensus and secured funding for a multicomponent intervention across hospital and post–acute care settings. Other studies have demonstrated the feasibility and efficacy of starting a single medication for a specific indication^7-9 (eg, methadone for opioid use disorder), yet strategies for expanding SUD services in hospitals and facilitating posthospital treatment linkages remain scarce.²¹ Our model addresses a widespread need and could be adapted to other hospitals, SUD treatment organizations, and Medicaid payers.

Our experience has several limitations. First, it took place at a single academic medical center in Oregon, a Medicaid expansion state. Second, our needs assessment involved a convenience sample of limited racial/ethnic diversity. Third, almost all patients had insurance, which could limit generalizability. Fourth, to secure funding, it was essential we had a clinical champion who was persuasive with hospital and CCO leadership; though increasing disease burden and skyrocketing costs² may drive administrators’ increased demand for ways to address SUD in hospitalized adults.

Our experience has several key implications. First, diverse partners were vital at all stages of program design, suggesting hospitals should look beyond traditional healthcare partners to address the SUD epidemic. Second, an interprofessional team that includes physicians, social workers, and peers may better engage patients and address complex system needs. Finally, a planned IMPACT evaluation will assess effects on substance use, healthcare use, and costs.

The United States faces a burgeoning SUD epidemic. Our experience describes an innovative care model and supports the idea that hospitals may play a leading role in convening partners, providing treatment, and driving population health improvements for adults with SUD.

Acknowledgment

The authors would like to acknowledge Peter Rapp and Thomas Yackel for leadership support; Tara Williams for administrative data support; Sarann Bielavitz and Naomi Wright for project management support, and Lynn Smith-Stott and Maria Michalczyk for help with model design. This work was presented at the American Society of Addiction Medicine national conference in Baltimore, MD in April 2016.

Disclosure

This work was funded by Oregon Health & Science University and CareOregon. The authors have no conflicts of interest to disclose.

Addiction is a national epidemic that represents both a pressing need and a significant burden to the healthcare system.¹ Hospitals are increasingly filled with people admitted for medical complications of substance use disorders (SUD).²People with SUD have longer lengths of stay (LOS) and high readmission rates.³ Hospitalization often does not address the root cause—the SUD. For example, many hospitals replace heart valves and deliver prolonged courses of intravenous (IV) antibiotics for endocarditis from injection drug use but do not offer addiction medicine consultation, medication for addiction treatment (MAT), or linkage to posthospital SUD treatment.^4,5

Hospitalization can provide reachable moments for initiating addiction care.⁶ Medications for opioid⁷ and alcohol use disorders⁸ can be started during hospitalization, promoting engagement in outpatient SUD care⁷ and increased uptake of MAT,^7-9 and reducing readmissions.^8,10 Yet, medications for SUD are underprescribed,^11,12 and most hospitals lack inpatient addiction medicine services and pathways to timely SUD care after discharge. Furthermore, traditional SUD treatment programs are often not equipped to manage medically complex patients or they have long waitlists.¹³ Most behavioral-physical health integration occurs in ambulatory settings. This fails to engage patients who do not access primary care. There is an urgent need for models that can improve care for hospitalized patients with SUD.

Here, we describe our experience using patient needs assessment to engage stakeholders and drive local systems change. We also describe the resulting care model, the Improving Addiction Care Team (IMPACT). Our experience provides a potentially useful example to other hospitals and communities seeking to address the national SUD epidemic.

Setting

In 2012, Oregon transformed its Medicaid system by establishing 16 regional “coordinated care organizations” (CCOs) to improve outcomes and slow healthcare spending.¹⁴ In a CCO environment, hospitals assume increased financial risk, yet reforms have focused on the outpatient setting. Therefore, executive leadership at Oregon Health & Science University (OHSU), an urban academic medical center, asked clinician-leaders to design point-of-care improvements for Medicaid-funded adults and build on existing models to improve care for socioeconomically vulnerable adults.^15,16 One priority that emerged was to make improvements for hospitalized adults with SUD. Of the adult inpatients at OHSU, 30% have Medicaid and 15% have SUD by administrative data alone. Before we started our work, OHSU lacked inpatient addiction medicine services.

Local Needs Assessment

To understand local needs and opportunities, we surveyed hospitalized adults with SUD. We used the electronic health record to generate a list of inpatients flagged by nurses for risky alcohol or drug use. A research assistant screened consecutive adults (≥18 years old) and invited those who screened positive for alcohol use (Alcohol Use Disorders Identification Test–Consumption [AUDIT-C])¹⁷ or drug use (single-item screener)¹⁸ to participate. We excluded non-English speakers, incarcerated adults, people using only marijuana or tobacco, psychiatry inpatients, and people unable to consent. Surveys assessed social and demographic factors, healthcare utilization, substance use severity, and treatment experience. Participants who reported high-risk illicit drug or alcohol use¹⁹ were asked to indicate their readiness to change on a 3-point scale developed for this study. Response range included: no interest, interest in cutting back, or interest in quitting. A subset of participants completed in-depth qualitative interviews exploring patient perceptions of substance use treatment needs.²⁰ We obtained hospital administrative data from hospital financial services.

Partner Engagement

We identified community partners with which we had an individual or organizational relationship and a common interest and potential for collaboration. All invited partners agreed to attend initial meetings. We convened leadership and frontline staff across partners. OHSU staff included hospital nursing and social work leaders; infectious disease, hospitalist, and addiction physicians; and health services researchers. Community organizations included Central City Concern (CCC), a community organization serving people facing homelessness and addiction; CODA, Inc., a nonprofit SUD treatment agency; and Coram/CVS infusion pharmacy.

Collectively, we reviewed needs assessment findings and examples from the literature^7-9 to develop strategies to address patient and system needs. We used patient narratives to foster alignment and prioritized areas in which integration could improve quality and costs. We assumed we would petition OHSU and/or Medicaid CCOs to finance efforts and saved potentially challenging budget discussions for later, when partnerships would be more developed. Our task force attended more than 3 large-group meetings and numerous small-group meetings to develop IMPACT.

Needs Assessment

Between September 2014 and April 2015, a research assistant approached 326 patients. Of these, 235 (72%) met study inclusion criteria, and 185 (78%) agreed to participate (Table 1). Of people who reported any substance use within the preceding 3 months, 58% of alcohol users and 67% of drug users said they were interested in cutting back or quitting. Fifty-four percent of participants with moderate- to high-risk opioid use and 16% with moderate- to high-risk alcohol use reported strong interest in MAT. In qualitative interviews, participants described inadequately treated withdrawal, the importance of trust and choice, and long wait times as a barriers to entering treatment after hospital discharge.²⁰

Administrative data revealed high rates of hospital readmissions and longer than expected LOS (Figure). Mean LOS was 10.26 days—4 days more than medicine patients’. Mean LOS was high among participants who required long-term IV antibiotics, particularly those with endocarditis or osteomyelitis (21.75 days; range, 1.00-51.00 days). We excluded one outlier with a 116-day hospitalization.

Intervention Design

Mapping needs to intervention components. We mapped needs assessment findings to 3 main IMPACT components: inpatient addiction medicine consultation service, pathways to posthospital SUD treatment, and medically enhanced residential treatment (MERT) (Table 2).

Inpatient addiction medicine consultation service. We developed this service to address patients’ report of high readiness to change and interest in starting MAT in the hospital. Community partners highlighted the need for peers to increase engagement and trust. Therefore, we included a physician, a social worker, and two peers on our team. The inpatient service engages patients, advises on withdrawal and pain, performs SUD assessments, initiates MAT, and provides counseling and treatment.

Funding the Intervention

We used administrative data to estimate potential savings and tailored a business case to CCO and hospital payers. The CCO business case centered on hospitalization as an opportunity to engage out-of-treatment adults and potentially reduce high-cost readmissions by managing physical and behavioral health needs. Working within budgeting time lines, we used data from the first 165 participants. These participants had 137 readmissions over a mean observation period of 4.5 months. Mean charge per readmission was $31,157 (range, $699-$206,596) and was highest for people with endocarditis (mean, $55,493; range, $23,204-$145,066) and osteomyelitis (mean, $68,774; range, $29,359-$124,481). We estimated that a 10% reduction in 6-month readmissions could avoid $674,863 in charges.

For the hospital, the primary financial incentive was reduced LOS. Given the possibility of shortening hospitalization through MERT, we estimated a 20% mean LOS reduction; for budgeting, we estimated a conservative 10% reduction. A 10% mean LOS reduction would free 205 bed-days (10% × 10.26 days mean LOS × 200 patients) and create space for another 32 inpatient admissions in year 1, assuming no change from medical patients’ 6.26 days mean LOS. The future of bundled payments further bolstered our business case, as did the potential to improve care quality, reduce nonproductive staff time, and increase institutional learning about SUD. Overall program costs approximated projected savings, and the hospital and a local CCO agreed to equally share the costs of the intervention (Table 2).

We have described an innovative approach to developing an SUD intervention for hospitalized adults. Using a process of broad stakeholder engagement, data-driven understanding of population needs, and analysis of financial incentives, we built consensus and secured funding for a multicomponent intervention across hospital and post–acute care settings. Other studies have demonstrated the feasibility and efficacy of starting a single medication for a specific indication^7-9 (eg, methadone for opioid use disorder), yet strategies for expanding SUD services in hospitals and facilitating posthospital treatment linkages remain scarce.²¹ Our model addresses a widespread need and could be adapted to other hospitals, SUD treatment organizations, and Medicaid payers.

Our experience has several limitations. First, it took place at a single academic medical center in Oregon, a Medicaid expansion state. Second, our needs assessment involved a convenience sample of limited racial/ethnic diversity. Third, almost all patients had insurance, which could limit generalizability. Fourth, to secure funding, it was essential we had a clinical champion who was persuasive with hospital and CCO leadership; though increasing disease burden and skyrocketing costs² may drive administrators’ increased demand for ways to address SUD in hospitalized adults.

Our experience has several key implications. First, diverse partners were vital at all stages of program design, suggesting hospitals should look beyond traditional healthcare partners to address the SUD epidemic. Second, an interprofessional team that includes physicians, social workers, and peers may better engage patients and address complex system needs. Finally, a planned IMPACT evaluation will assess effects on substance use, healthcare use, and costs.

The United States faces a burgeoning SUD epidemic. Our experience describes an innovative care model and supports the idea that hospitals may play a leading role in convening partners, providing treatment, and driving population health improvements for adults with SUD.

Acknowledgment

The authors would like to acknowledge Peter Rapp and Thomas Yackel for leadership support; Tara Williams for administrative data support; Sarann Bielavitz and Naomi Wright for project management support, and Lynn Smith-Stott and Maria Michalczyk for help with model design. This work was presented at the American Society of Addiction Medicine national conference in Baltimore, MD in April 2016.

Disclosure

This work was funded by Oregon Health & Science University and CareOregon. The authors have no conflicts of interest to disclose.

Addiction is a national epidemic that represents both a pressing need and a significant burden to the healthcare system.¹ Hospitals are increasingly filled with people admitted for medical complications of substance use disorders (SUD).²People with SUD have longer lengths of stay (LOS) and high readmission rates.³ Hospitalization often does not address the root cause—the SUD. For example, many hospitals replace heart valves and deliver prolonged courses of intravenous (IV) antibiotics for endocarditis from injection drug use but do not offer addiction medicine consultation, medication for addiction treatment (MAT), or linkage to posthospital SUD treatment.^4,5

Hospitalization can provide reachable moments for initiating addiction care.⁶ Medications for opioid⁷ and alcohol use disorders⁸ can be started during hospitalization, promoting engagement in outpatient SUD care⁷ and increased uptake of MAT,^7-9 and reducing readmissions.^8,10 Yet, medications for SUD are underprescribed,^11,12 and most hospitals lack inpatient addiction medicine services and pathways to timely SUD care after discharge. Furthermore, traditional SUD treatment programs are often not equipped to manage medically complex patients or they have long waitlists.¹³ Most behavioral-physical health integration occurs in ambulatory settings. This fails to engage patients who do not access primary care. There is an urgent need for models that can improve care for hospitalized patients with SUD.

Here, we describe our experience using patient needs assessment to engage stakeholders and drive local systems change. We also describe the resulting care model, the Improving Addiction Care Team (IMPACT). Our experience provides a potentially useful example to other hospitals and communities seeking to address the national SUD epidemic.

Setting

In 2012, Oregon transformed its Medicaid system by establishing 16 regional “coordinated care organizations” (CCOs) to improve outcomes and slow healthcare spending.¹⁴ In a CCO environment, hospitals assume increased financial risk, yet reforms have focused on the outpatient setting. Therefore, executive leadership at Oregon Health & Science University (OHSU), an urban academic medical center, asked clinician-leaders to design point-of-care improvements for Medicaid-funded adults and build on existing models to improve care for socioeconomically vulnerable adults.^15,16 One priority that emerged was to make improvements for hospitalized adults with SUD. Of the adult inpatients at OHSU, 30% have Medicaid and 15% have SUD by administrative data alone. Before we started our work, OHSU lacked inpatient addiction medicine services.

Local Needs Assessment

To understand local needs and opportunities, we surveyed hospitalized adults with SUD. We used the electronic health record to generate a list of inpatients flagged by nurses for risky alcohol or drug use. A research assistant screened consecutive adults (≥18 years old) and invited those who screened positive for alcohol use (Alcohol Use Disorders Identification Test–Consumption [AUDIT-C])¹⁷ or drug use (single-item screener)¹⁸ to participate. We excluded non-English speakers, incarcerated adults, people using only marijuana or tobacco, psychiatry inpatients, and people unable to consent. Surveys assessed social and demographic factors, healthcare utilization, substance use severity, and treatment experience. Participants who reported high-risk illicit drug or alcohol use¹⁹ were asked to indicate their readiness to change on a 3-point scale developed for this study. Response range included: no interest, interest in cutting back, or interest in quitting. A subset of participants completed in-depth qualitative interviews exploring patient perceptions of substance use treatment needs.²⁰ We obtained hospital administrative data from hospital financial services.

Partner Engagement

We identified community partners with which we had an individual or organizational relationship and a common interest and potential for collaboration. All invited partners agreed to attend initial meetings. We convened leadership and frontline staff across partners. OHSU staff included hospital nursing and social work leaders; infectious disease, hospitalist, and addiction physicians; and health services researchers. Community organizations included Central City Concern (CCC), a community organization serving people facing homelessness and addiction; CODA, Inc., a nonprofit SUD treatment agency; and Coram/CVS infusion pharmacy.

Collectively, we reviewed needs assessment findings and examples from the literature^7-9 to develop strategies to address patient and system needs. We used patient narratives to foster alignment and prioritized areas in which integration could improve quality and costs. We assumed we would petition OHSU and/or Medicaid CCOs to finance efforts and saved potentially challenging budget discussions for later, when partnerships would be more developed. Our task force attended more than 3 large-group meetings and numerous small-group meetings to develop IMPACT.

Needs Assessment

Between September 2014 and April 2015, a research assistant approached 326 patients. Of these, 235 (72%) met study inclusion criteria, and 185 (78%) agreed to participate (Table 1). Of people who reported any substance use within the preceding 3 months, 58% of alcohol users and 67% of drug users said they were interested in cutting back or quitting. Fifty-four percent of participants with moderate- to high-risk opioid use and 16% with moderate- to high-risk alcohol use reported strong interest in MAT. In qualitative interviews, participants described inadequately treated withdrawal, the importance of trust and choice, and long wait times as a barriers to entering treatment after hospital discharge.²⁰

Administrative data revealed high rates of hospital readmissions and longer than expected LOS (Figure). Mean LOS was 10.26 days—4 days more than medicine patients’. Mean LOS was high among participants who required long-term IV antibiotics, particularly those with endocarditis or osteomyelitis (21.75 days; range, 1.00-51.00 days). We excluded one outlier with a 116-day hospitalization.

Intervention Design

Mapping needs to intervention components. We mapped needs assessment findings to 3 main IMPACT components: inpatient addiction medicine consultation service, pathways to posthospital SUD treatment, and medically enhanced residential treatment (MERT) (Table 2).

Inpatient addiction medicine consultation service. We developed this service to address patients’ report of high readiness to change and interest in starting MAT in the hospital. Community partners highlighted the need for peers to increase engagement and trust. Therefore, we included a physician, a social worker, and two peers on our team. The inpatient service engages patients, advises on withdrawal and pain, performs SUD assessments, initiates MAT, and provides counseling and treatment.

Funding the Intervention

We used administrative data to estimate potential savings and tailored a business case to CCO and hospital payers. The CCO business case centered on hospitalization as an opportunity to engage out-of-treatment adults and potentially reduce high-cost readmissions by managing physical and behavioral health needs. Working within budgeting time lines, we used data from the first 165 participants. These participants had 137 readmissions over a mean observation period of 4.5 months. Mean charge per readmission was $31,157 (range, $699-$206,596) and was highest for people with endocarditis (mean, $55,493; range, $23,204-$145,066) and osteomyelitis (mean, $68,774; range, $29,359-$124,481). We estimated that a 10% reduction in 6-month readmissions could avoid $674,863 in charges.

For the hospital, the primary financial incentive was reduced LOS. Given the possibility of shortening hospitalization through MERT, we estimated a 20% mean LOS reduction; for budgeting, we estimated a conservative 10% reduction. A 10% mean LOS reduction would free 205 bed-days (10% × 10.26 days mean LOS × 200 patients) and create space for another 32 inpatient admissions in year 1, assuming no change from medical patients’ 6.26 days mean LOS. The future of bundled payments further bolstered our business case, as did the potential to improve care quality, reduce nonproductive staff time, and increase institutional learning about SUD. Overall program costs approximated projected savings, and the hospital and a local CCO agreed to equally share the costs of the intervention (Table 2).

We have described an innovative approach to developing an SUD intervention for hospitalized adults. Using a process of broad stakeholder engagement, data-driven understanding of population needs, and analysis of financial incentives, we built consensus and secured funding for a multicomponent intervention across hospital and post–acute care settings. Other studies have demonstrated the feasibility and efficacy of starting a single medication for a specific indication^7-9 (eg, methadone for opioid use disorder), yet strategies for expanding SUD services in hospitals and facilitating posthospital treatment linkages remain scarce.²¹ Our model addresses a widespread need and could be adapted to other hospitals, SUD treatment organizations, and Medicaid payers.

Our experience has several limitations. First, it took place at a single academic medical center in Oregon, a Medicaid expansion state. Second, our needs assessment involved a convenience sample of limited racial/ethnic diversity. Third, almost all patients had insurance, which could limit generalizability. Fourth, to secure funding, it was essential we had a clinical champion who was persuasive with hospital and CCO leadership; though increasing disease burden and skyrocketing costs² may drive administrators’ increased demand for ways to address SUD in hospitalized adults.

Our experience has several key implications. First, diverse partners were vital at all stages of program design, suggesting hospitals should look beyond traditional healthcare partners to address the SUD epidemic. Second, an interprofessional team that includes physicians, social workers, and peers may better engage patients and address complex system needs. Finally, a planned IMPACT evaluation will assess effects on substance use, healthcare use, and costs.

The United States faces a burgeoning SUD epidemic. Our experience describes an innovative care model and supports the idea that hospitals may play a leading role in convening partners, providing treatment, and driving population health improvements for adults with SUD.

Acknowledgment

The authors would like to acknowledge Peter Rapp and Thomas Yackel for leadership support; Tara Williams for administrative data support; Sarann Bielavitz and Naomi Wright for project management support, and Lynn Smith-Stott and Maria Michalczyk for help with model design. This work was presented at the American Society of Addiction Medicine national conference in Baltimore, MD in April 2016.

Disclosure

This work was funded by Oregon Health & Science University and CareOregon. The authors have no conflicts of interest to disclose.