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The july effect: An analysis of never events in the nationwide inpatient sample

Author(s)

Timothy Wen, MPH

Frank J. Attenello, MD

The simultaneous arrival of new residents, medical students, and faculty in July each year results in a complex transition period for hospitals. Medical centers strive to deliver high‐quality and efficient care while undergoing these cyclical changes, with over 100,000 interns/residents in the United States taking part in this changeover.[1] This period is hypothesized to hold an increased risk of adverse outcomes referred to as the July Effect.[1, 2, 3, 4, 5] Although studies have reported associated increases in mortality risk, decreases in efficiency, and an increase in undesirable events during this time, occurrences are still debated.[1, 3, 4, 5]

In 2008, the Centers for Medicare & Medicaid Services (CMS) published and instituted a nationwide series of never events. These events, narrowed to a list of hospital‐acquired complications (HACs), are characterized as iatrogenic adverse outcomes and deemed preventable and egregious. Medicare has subsequently withheld reimbursement for additional cost of treatment related to the events.[6, 7, 8] HACs include complications such as air embolism, retained foreign body, blood incompatibility, pressure ulcer, catheter‐associated urinary tract infection (UTI), vascular catheter‐associated infection, manifestations of poor glycemic control, falls/trauma, deep venous thrombosis or pulmonary embolism after total knee and hip replacements, surgical site infections after coronary artery bypass graft, and surgical site infections after certain orthopedic or bariatric surgeries. Prior studies have utilized HACs as a metric for quality of healthcare delivery in subspecialties such as cerebrovascular surgery, bowel surgery, and urology.[6, 9, 10]

Though the July effect has been assessed across multiple specialties and hospitals, no prior studies have evaluated this phenomenon on a national level and incorporated all hospital admission diagnoses. Through this study, we aim to provide insight into this relatively new quality metric when evaluating admissions made during the early months of the academic year. This study's primary aim is to evaluate the frequency of HAC occurrence across hospital discharges on a national level as a function of admission month after the initiation of the nonreimbursable nature of the CMS never events in 2008. Furthermore, the secondary aims of this study examine the impact of the July effect on inpatient length of stay (LOS) and charges. We hypothesized that July admission is associated with an increases in HAC occurrence, LOS, and inpatient charges.

METHODS

Data Source

An observational study was conducted using data extracted from the Nationwide Inpatient Sample (NIS) years 2008 to 2011. NIS is an annually compiled database maintained by the Agency for Healthcare Research and Quality and contains information on more than 8 million hospital admissions each year from more than 40 states and 1000 hospitals.[11] The database represents 20% of all US hospital discharges and contains a weighting system that allows for calculation of population estimates.[11]

Patient Sample

All patients who were admitted to a hospital from 2008 to 2011 were included in this study. NIS does not contain unique patient identifiers; thus, each discharge was treated as an independent event, even if it may have represented a repeat hospitalization by the same patient. Each hospitalization contained patient and hospital factors that were included as covariates for analysis. Patient factors such as race (white, black, Hispanic, Asian/Pacific Islander, Native American, other), payer information (Medicare, Medicaid, private insurance, no charge, self‐pay, other), and gender (male, female) were included as categorical variables. Other patient covariates of interest included age (recoded from a continuous to a categorical variable with the following groupings: <18, 19 to 30, 31 to 40, 41 to 50, 50 to 65, 66 to 80, and >80 years) and number of comorbidities (none, 1, 2 or more). The comorbidities variable was drawn from the NIS database and was derived directly from the Elixhauser comorbidity index that is often cited in other studies as a risk‐adjustment measure.[12] Hospital factors, such as bed size (small [<200], medium [201400], large [>400 beds]), teaching status (teaching, nonteaching), hospital region (Northeast, Midwest, South, West), and location (rural, urban) were included in the analysis as categorical variables. Variables with missing values were encoded as a missing category for all exposure variables.

Outcomes

The primary outcome of interest was the probability of HAC occurrence. The frequency of HAC occurrence in July was compared to that of other months. HACs were defined using the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD‐9‐CM) codes and verified through CMS literature and data.[13] Demographics of the patient and hospital variables, as well as the frequency of HACs were tabulated. Secondary outcomes included the likelihood of incurring higher inpatient charges and experiencing a prolonged LOS, defined as at or above the 90th percentile for both variables.

Statistical Analyses

Demographics were calculated using survey‐adjusted univariate frequency and means analysis. Multivariable logistic regressions were modeled using survey‐adjusted generalized estimating equations to assess the outcomes described above. Each model was adjusted for hospital (bed size, teaching status, hospital region, hospital location) and patient (race, payer information, gender, age, number of comorbidities) factors. The models assessing the prolonged LOS and higher inpatient charges outcomes were adjusted with the same patient and hospital factors, with the addition of HAC occurrence as a covariate. The main exposure of interest in this study was admission in the month of July. Admission month is included as a multilevel variable and recoded into a dichotomous variable.

Aside from hospital and patient covariates, multivariate analyses were also adjusted according to severity of admission. Admission severity was defined using three variables: All‐Patient Refined Disease‐Related Group (APR‐DRG), admission type, and admission source. 3M's APR‐DRG algorithm (3M Health Information Systems, Wallingford, CT) is a system of risk adjustment methods developed by 3M and based upon the existing DRG structure and used in a number of other NIS studies as a valid measure of admission severity.[14, 15, 16, 17] The algorithm divides patient admissions into 500 categories of similar clinical and resource utilization features. APR‐DRGs in the NIS are categorized into five classifications: no class specified, minor loss of function, moderate loss of function, major loss of function, and extreme loss of function. Additionally, admission type (emergency, urgent, elective, newborn, trauma center, other), and admission source (emergency department, another hospital, other health facility, court/law enforcement, routine) were coded in the NIS. Together, these three variables were utilized as covariates in all multivariable logistic regression models to adjust for the severity of injuries patients harbored prior to admission.

In addition to our primary analyses, we conducted a series of secondary analyses. We conducted survey‐adjusted multivariable logistic regression analysis with the primary predictor of interest being individual months, with July as a reference group and the outcome of HAC occurrence. We further analyzed our primary exposure of July versus non‐July admissions and stratified it by the presence of an operating room procedure as a surrogate measure of surgical versus nonsurgical admissions. We also analyzed LOS and total charges as continuous outcomes to elucidate the precise impact of HACs and July admission. Finally, to address the issue of missing values, we conducted a four‐step multiple imputation for complex data with categorical variables using the methods outlined by Berglund et al. [18] In doing so, we created five imputed datasets using a Markov Chain Monte Carol method, producing monotone missing data patterns for a four‐step procedure. We imputed the missing data using the monotone logistic facet of the multiple imputation model. We then used survey‐adjusted logistic regression to estimate odds ratios (ORs) for each of the imputed datasets. Finally, we combined the results from the five logistic regression models by fully incorporating the variance adjustment from both logistic regressions and multiple imputations (PROC MIANALYZE).[18] These analyses were similarly adjusted for with the same patient, hospital, and severity demographics adjusted for in the original model.

Statistical significance was achieved with a P value <0.05. All descriptive and logistic regression analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

Overview Demographics

There were 143,019,381 inpatient admissions between 2008 and 2011 in the NIS. Overall, 4.7% (6,738,949) of all US hospital inpatient admissions had incurred at least 1 HAC (Table 1). Approximately 7.6% of inpatient admissions occurred in July, whereas 83.5% occurred in the months of August to June (8.9% of data is missing). July admits had a higher overall frequency of HAC occurrence compared to non‐July admissions (4.9% vs 4.7%). There were marginal differences between hospital and patient factors associated with admissions in July compared to those in other months. The majority of patients in both July and non‐July admissions were between 66 and 80 years old (Table 1).

Table 1.Population Demographics, 2008 to 2011
	July Admission, n=12,003,545		Non‐July Admission, n=131,015,837
	N	%	N	%
NOTE: Abbreviations: API, Asian/Pacific Islander; DRG, Diagnosis‐Related Group; HAC, hospital‐acquired complication. a Numbers are rounded to the 1,000th place.
Patient demographic factors
HAC occurrence
HAC during admission	594,000	4.9%	6,145,000	4.7%
No HAC during admission	11,410,000	95.1%	124,871,000	95.3%
Race
White	6,783,000	56.5%	74,222,000	56.7%
Black	1,468,000	12.2%	15,993,000	12.2%
Hispanic	1,231,000	10.3%	13,186,000	10.1%
API	288,000	2.4%	3,142,000	2.4%
Native American	77,000	0.6%	867,000	0.7%
Other	360,000	3.0%	3,931,000	3.0%
Missing	1,798,000	15.0%	19,675,000	15.0%
Payer information
Medicare	4,401,000	36.7%	49,209,000	37.6%
Medicaid	2,418,000	20.1%	25,977,000	19.8%
Private insurance	4,084,000	34.0%	44,106,000	33.7%
Self‐pay	636,000	5.3%	6,693,000	5.1%
No charge	43,000	0.4%	445,000	0.3%
Other	393,000	3.3%	4,261,000	3.3%
Missing	28,000	0.2%	323,000	0.2%
Comorbidities
No comorbidities	3,957,000	33.0%	42,249,000	32.2%
1	2,104,000	17.5%	23,209,000	17.7%
2 or more	5,943,000	49.5%	65,557,000	50.0%
Age category
18 years	1,965,000	16.4%	21,702,000	16.6%
1930 years	1,482,000	12.3%	15,385,000	11.7%
3040 years	1,156,000	9.6%	12,091,000	9.2%
4050 years	1,196,000	10.0%	12,737,000	9.7%
5065 years	2,323,000	19.4%	25,458,000	19.4%
6580 years	2,345,000	19.5%	26,218,000	20.0%
>80 years	1,536,000	12.8%	17,424,000	13.3%
Gender
Female	6,994,000	58.3%	76,146,000	58.1%
Male	4,984,000	41.5%	54,571,000	41.7%
Missing	26,000	0.2%	300,000	0.2%
Hospital demographic factors
Hospital region
Northeast	2,561,000	21.3%	27,650,000	21.1%
Midwest	3,007,000	25.1%	32,799,000	25.0%
South	3,878,000	32.3%	42,696,000	32.6%
West	2,557,000	21.3%	27,872,000	21.3%
Hospital location
Rural	1,507,000	12.6%	16,760,000	12.8%
Urban	10,348,000	86.2%	112,639,000	86.0%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital teaching status
Nonteaching	6,129,000	51.1%	67,447,000	51.5%
Teaching	5,726,000	47.7%	61,952,000	47.3%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital bed size
Small	1,496,000	12.5%	16,479,000	12.6%
Medium	2,905,000	24.2%	31,800,000	24.3%
Large	7,453,000	62.1%	81,120,000	61.9%
Missing	149,000	1.2%	1,617,000	1.2%
Admission severity factors
Admission source
Emergency department	1,078,000	9.0%	12,425,000	9.5%
Another hospital	110,000	0.9%	1,219,000	0.9%
Other health facility	61,000	0.5%	664,000	0.5%
Court/law enforcement	3,000	0.0%	35,000	0.0%
Routine	1,545,000	12.9%	16,529,000	12.6%
Missing	9,205,000	76.7%	100,144,000	76.4%
Admission type
Emergency	4,842,000	40.3%	53,386,000	40.7%
Urgent	1,985,000	16.5%	21,747,000	16.6%
Elective	2,570,000	21.4%	28,276,000	21.6%
Newborn	1,130,000	9.4%	11,625,000	8.9%
Trauma	57,000	0.5%	508,000	0.4%
Other	4,000	0.0%	45,000	0.0%
Missing	1,417,000	11.8%	15,430,000	11.8%
All‐Patient Refined DRG, severity
No class specified	10,000	0.1%	132,000	0.1%
Minor loss of function	4,289,000	35.7%	46,092,000	35.2%
Moderate loss of function	4,313,000	35.9%	47,150,000	36.0%
Major loss of function	2,630,000	21.9%	28,939,000	22.1%
Extreme loss of function	762,000	6.3%	8,704,000	6.6%

The most commonly occurring HACs were falls (5,863,778), pressure ulcers (731,103), vascular catheter‐associated infections (364,204), and catheter UTIs (290,207). HAC frequency showed a marked increase from 2008 to 2011.

HAC Occurrence

Multivariate logistic regression demonstrated that the likelihood of having one or more HACs was 6% higher in July admits compared to non‐July admits, adjusting for patient and hospital covariates (OR=1.06, 95% confidence interval [CI]: 1.061.07, P<0.0001). However, admission during July was not the most significant predictor of an HAC occurrence (Table 2). Institutional factors, such as teaching hospitals (OR=1.22, 95% CI: 1.161.28, P<0.001 vs nonteaching hospitals) and large (OR=1.11, 95% CI: 1.061.17, P=0.0002 vs small bed‐size facilities) and medium‐sized facilities (OR=1.06, 95% CI: 1.001.13, P=0.0461 vs small bed‐size facilities) were the most powerful predictors of HAC occurrence during an inpatient hospitalization (Table 2). Additionally, in a separate subanalysis with the month of admission as the primary exposure of interest, we noted that each month except for August demonstrated statistically significant decreased odds of HAC occurrence when compared to July (see Supporting Table 1 in the online version of this article). As the adjusted HAC likelihood was not statistically different between August and July, an additional analysis was run with the primary exposure being July and August admission versus all other months of admission. These resulted in a finding of 7% increased likelihood of HAC occurrence among July and August admissions compared to all others (OR=1.07, 95% CI: 1.061.07, P<0.0001; see Supporting Table 2 in the online version of this article). Similarly, a multiple imputation model adjusting for missing values produced a very similar July effect estimate to the nonimputed model (OR=1.06, 95% CI: 1.031.09, P<0.01).

Table 2.July Admission on Hospital‐Acquired Complication Occurrence Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: API, Asian/Pacific Islander; CI, confidence interval; DRG, DiagnosisRelated Group; OR, odds ratio.
Patient demographic factors
Admission time
July admit	1.06	1.061.07	<0.0001
Non‐July admit	Reference
Race
White	Reference
Black	0.78	0.750.80	<0.0001
Hispanic	0.81	0.760.85	<0.0001
API	0.75	0.710.80	<0.0001
Native American	0.92	0.831.02	0.1256
Other	0.91	0.840.98	<0.0001
Payer information
Medicare	1.00	0.971.02	0.7151
Medicaid	0.87	0.830.90	<0.0001
Private insurance	Reference
Selfpay	1.27	1.201.33	<0.0001
No charge	1.07	0.921.23	0.3871
Other	1.93	1.822.05	<0.0001
Comorbidities
No comorbidities	Reference
1	0.84	0.820.86	<0.0001
2 or more	0.70	0.680.72	<0.0001
Age category
18 years	0.35	0.330.37	<0.0001
1930 years	0.33	0.320.35	<0.0001
3040 years	0.32	0.310.33	<0.0001
4050 years	0.36	0.350.37	<0.0001
5065 years	0.37	0.360.38	<0.0001
6580 years	0.45	0.450.46	<0.0001
>80 years	Reference
Gender
Female	0.92	0.900.93	<0.0001
Male	Reference
Hospital demographic factors
Hospital region
Northeast	Reference
Midwest	1.06	1.001.13	0.2563
South	1.11	1.061.22	0.0005
West	1.08	0.971.20	0.1431
Hospital location
Rural	Reference
Urban	1.01	0.961.06	0.7144
Hospital teaching status
Nonteaching	Reference
Teaching	1.22	1.161.28	<0.0001
Hospital bed size
Small	Reference
Medium	1.06	1.001.13	0.0461
Large	1.11	1.061.17	0.0002
Admission severity factors
Admission source
Emergency department	1.63	1.481.80	<0.0001
Another hospital	1.96	1.762.17	<0.0001
Other health facility	1.62	1.302.03	<0.0001
Court/law enforcement	1.37	1.011.85	0.0438
Routine	Reference
Admission type
Emergency	2.15	2.032.28	<0.0001
Urgent	1.28	1.201.35	<0.0001
Elective	Reference
Newborn	0.69	0.630.76	<0.0001
Trauma	>1000	<0.001>1000	0.9962
Other	0.91	0.531.55	0.7183
AllPatient Refined DRG, severity
No class specified	0.73	0.620.85	<0.0001
Minor loss of function	Reference
Moderate loss of function	1.14	1.121.16	<0.0001
Major loss of function	1.61	1.571.66	<0.0001
Extreme loss of function	4.65	4.504.80	<0.0001

We utilized similar models adjusting for the same patient, hospital, and severity factors in teaching hospital population. Patients discharged from teaching hospitals were 7% more likely to incur an HAC during admission in July compared to those admitted in the other months (OR=1.07, 95% CI: 1.061.08, P<0.01).

Higher Inpatient Charges and Prolonged LOS

The presence of one or more HACs was a significant predictor for higher inpatient charges (Table 3; OR=1.81, 95% CI: 1.74‐1.87, P<0.0001), when adjusting for July admission, patient and hospital factors, and admission severity. HAC occurrence was also a significant predictor of prolonged LOS (Table 3; OR=1.45, 95% CI: 1.42‐1.48, P<0.0001). Mean inpatient charges and LOS in this sample were $33,662.00 and 4.6 days. Patients with at least 1 HAC had a mean inpatient charge of $61,457.00, whereas those with no HAC had a mean charge of $32,377.00. Furthermore, LOS was prolonged in patients with HACs versus those who did not have HACs during hospitalization (7.14 days vs 4.49 days). Our regression analyses indicated that HAC patients had 1.48 (P<0.0001) more days of LOS and $18,258.00 (P<0.0001) more in total charges.

Table 3.Higher Inpatient Costs and Prolonged LOS Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: CI, confidence interval; HAC, hospital‐acquired complication; LOS, length of stay; OR, odds ratio.
Higher inpatient costs
Admission time
July admit	1.00	0.991.01	0.9693
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.81	1.741.87	<0.0001
No HAC occurrence	Reference
Prolonged LOS
Admission time
July admit	0.98	0.980.99	<0.0001
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.45	1.421.48	<0.0001
No HAC occurrence	Reference

DISCUSSION

This study analyzes the relationship between admission month and the incidence of HACs in a national sample. This study is also among the first to examine preventable complications as a measure of inefficiencies and inexperience of new staff during staff turnover in the month of July. In our retrospective cohort study of more than 100 million admissions across 4 years, we found a 4.9% prevalence of HACs among July admits compared to 4.7% in the non‐July admission population. In multivariate analysis, July admissions were associated with a 6% increased likelihood of HACs. Such data are concordant with other studies that demonstrate a positive July effect on mortality and efficiency.[3, 19] Evaluation of a surgical cohort revealed an 18% increase in risk‐adjusted surgical morbidity and a 41% increase in risk‐adjusted surgical mortality during July and August, using the American College of Surgeons' National Surgical Quality Improvement Program.[20] Though several studies have noted worsened outcomes during the month of July, results have been mixed. Several studies in subspecialty populations or local databases suggest no clear increase in mortality or complication rate.[3, 4, 5, 21, 22, 23, 24, 25] This current study is the first to examine these relationships using HACs as a surrogate measure indicative of quality of care and safety of new staff in the month of July.

Although multiple investigations studying July admission use mortality as an outcome measure, evaluation of preventable hospital complications may actually be more reflective of the impact of new staff on care quality and safety.[20, 26, 27, 28, 29] Mortality rates can be significantly confounded by patient‐specific factors, such as disease severity and comorbidities, whereas iatrogenic adverse events, such as HACs, are postulated to be more reflective of errors in systems and processes within the healthcare delivery institution. For example, studies demonstrate that anesthetic procedures that do not result in mortality are often associated with significant increases in complications such as central and peripheral nerve injuries, inadequate oxygenation, perioperative vomiting/aspiration, and technical failures of tracheal tube placement.[1] It is therefore not surprising that studies show July admissions are associated with longer LOS and duration of procedure, in addition to increased hospital charges.[3]

We did attempt to adjust for the effect of disease severity on HACs by incorporating 3M's APR‐DRG system, admission source, and admission type into our multivariate analyses. After adjusting for disease severity in our multivariate analysis, July admission maintained a statistically significant association with increased HAC incidence.

In our secondary analyses, we noted that all months except for August experienced significantly decreased odds of HAC occurrence compared to July admissions with similar magnitudes of likelihood found when combining July and August admissions versus all others (see Supporting Tables 1 and 2 in the online version of this article). This spillover finding may indicate the learning curve of inexperienced and new hospital staff and also suggest that the July effect is not limited only to the month of July. However, because the magnitudes of the 2 models (Table 2; Supporting Table 2 in the online version of this article) are so similar, we continue to refer to this phenomena as the July effect, with the known implication that there is a continuance beyond July and into August.

It is of interest to note that when the analysis was subsetted to only teaching institutions, July admissions in the teaching hospital cohort showed significant increases in HAC likelihood compared to non‐July admissions. Although studies suggest that inexperienced residents contribute to patient complications, the increased rate of HACs in July admits may also be multifactorial.[30] It is likely that the need for new healthcare staff to gain experience, familiarity, and effective communication also influences the HAC rate. The impact of nursing and ancillary staff involvement in the prevention of HACs is crucial. Although the July effect was primarily focused on physician elements, the nursing and ancillary staff elements are more clearly noted when evaluating HACs as an outcome. Pilot studies including multidisciplinary hospital, nursing, and physician teams, involving a significant effort to streamline communication and established protocols, have resulted in drastic decreases in patient falls, the most common of the HAC occurrences.[31] The increased hires during this time period (as new physicians and nurses complete training in June) accompanied with the need to acclimate these groups to one another and to train them on established protocols may result in risks for HACs not previously noted when evaluating more standard outcome measures.

Separate studies have shown worsened outcomes for July surgical admissions in large databases, with results indicating longer operative times for July admissions, inpatient mortality, intraoperative complications, and postoperative morbidity in areas like cardiac or spinal surgeries.[20, 22, 26, 32, 33, 34] Similarly, other studies noted that medical admissions also demonstrated worse outcomes for July admissions, resulting in increased fatal medication errors, preventable complications, and worse documentation errors.[21, 28, 30, 35, 36] In the present study, July admissions demonstrated an increased likelihood of HACs when stratified by surgical and medical admissions, as seen in the current literature.

Our study also indicates that surgical patients are noted to have a 2% increase in HACs during July versus a 9% July increase in medical patients. To the authors' knowledge, this is one of the first studies to stratify a patient population by surgical and medical services to evaluate the effect of July admission on outcomes. Possible explanations are that surgical candidates are often medically optimized prior to elective procedures, requiring a stringent protocol to be executed prior to performing an operation on a patient. Thus, the surgical patients are inherently prescreened to be of better overall health to be deemed operable compared to the traditional medicine patient. Rich et al. ([36]) performed a comparison analysis of multiple services, noting that patients with internal medicine diagnoses demonstrated the expected July effect with declines in diagnostic and pharmaceutical changes throughout the year as an indicator of improved experience leading to decreased utilization.[36] However, in that same study, the authors noted no discernible July effect among surgical patients, possibly related to the difference in resource utilization emphasized in the medical versus surgical programs.[36]

The presence of one or more HACs was a significant predictor for higher inpatient charges, when adjusting for July admission and patient/hospital factors. HAC occurrence was also a significant predictor of prolonged LOS. This is in concordance with multiple prior studies noting the association of higher LOS with HAC occurrence.[37, 38, 39, 40, 41, 42] This further supports the elevated HAC‐associated burden predicted by the CMS when compiling specific HACs.[38, 39, 40, 41, 42] Further studies in the coming years may determine whether CMS HAC regulation translates to decreased inpatient admissions durations and cost reductions over time.

This study has several limitations largely associated with the use of a standardized national database. Coding of HACs depends on consistent and accurate reporting, with errors resulting in information bias. Estimates regarding ICD‐9‐CM coding in the NIS have been cited as approximately 80% accurate.[43] Furthermore, missing variables, though noted in results, and the heterogeneity of the study population, may influence the data. Unfortunately, the nature of the data collection throughout NIS practices is not uniform within states, which may explain why a percentage of data is missing. Because of this data structure, NIS does not have documentation of the month during which an HAC was noted for admissions spanning multiple months. In regard to the missing data, we attempted to account for this using a multiple imputation model that generated similar results to our original model with missing categories coded into it; with the only major difference being expectedly larger standard errors.[44] With yearly changes in CMS coding, the addition and familiarity with new codes may influence analysis over time. Of note, the HAC denoting pressure ulcer did not exist before 2009. We were also unable to use splines to incorporate a time‐series method, as the focus of our study was targeted to looking at the higher incidences of HACs associated with July admission and not a temporal trend prior to and after July, and also the limitations we had in number of time points required for a proper time series analysis.[45] Finally, HACs are only capable of evaluating inpatient events and omit events occurring after discharge.

CONCLUSIONS

These data reveal an increase in HAC frequency during the month of July in a large national sample of patients. Recognition of a noted statistical trend in July may direct necessary attention to a time associated with increased occurrence of preventable iatrogenic adverse events. The HACs represent potential breakdowns in organizational structure distinct from traditional measures of safety, such as mortality and specialty‐specific morbidity. New guidelines dedicated to improving HACs during this time may help to decrease prevalence in both teaching and nonteaching hospitals.

Disclosure

Nothing to report.

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References

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Article PDF

Issue

Journal of Hospital Medicine - 10(7)

Page Number

432-438

Read more about July Effect on Never Events

Sections

Original Research

Author(s)

Timothy Wen, MPH

Frank J. Attenello, MD

Author(s)

Frank J. Attenello, MD

Files

Files

Article PDF

Article PDF

METHODS

Data Source

Patient Sample

Outcomes

Statistical Analyses

Statistical significance was achieved with a P value <0.05. All descriptive and logistic regression analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

Overview Demographics

Table 1.Population Demographics, 2008 to 2011
	July Admission, n=12,003,545		Non‐July Admission, n=131,015,837
	N	%	N	%
NOTE: Abbreviations: API, Asian/Pacific Islander; DRG, Diagnosis‐Related Group; HAC, hospital‐acquired complication. a Numbers are rounded to the 1,000th place.
Patient demographic factors
HAC occurrence
HAC during admission	594,000	4.9%	6,145,000	4.7%
No HAC during admission	11,410,000	95.1%	124,871,000	95.3%
Race
White	6,783,000	56.5%	74,222,000	56.7%
Black	1,468,000	12.2%	15,993,000	12.2%
Hispanic	1,231,000	10.3%	13,186,000	10.1%
API	288,000	2.4%	3,142,000	2.4%
Native American	77,000	0.6%	867,000	0.7%
Other	360,000	3.0%	3,931,000	3.0%
Missing	1,798,000	15.0%	19,675,000	15.0%
Payer information
Medicare	4,401,000	36.7%	49,209,000	37.6%
Medicaid	2,418,000	20.1%	25,977,000	19.8%
Private insurance	4,084,000	34.0%	44,106,000	33.7%
Self‐pay	636,000	5.3%	6,693,000	5.1%
No charge	43,000	0.4%	445,000	0.3%
Other	393,000	3.3%	4,261,000	3.3%
Missing	28,000	0.2%	323,000	0.2%
Comorbidities
No comorbidities	3,957,000	33.0%	42,249,000	32.2%
1	2,104,000	17.5%	23,209,000	17.7%
2 or more	5,943,000	49.5%	65,557,000	50.0%
Age category
18 years	1,965,000	16.4%	21,702,000	16.6%
1930 years	1,482,000	12.3%	15,385,000	11.7%
3040 years	1,156,000	9.6%	12,091,000	9.2%
4050 years	1,196,000	10.0%	12,737,000	9.7%
5065 years	2,323,000	19.4%	25,458,000	19.4%
6580 years	2,345,000	19.5%	26,218,000	20.0%
>80 years	1,536,000	12.8%	17,424,000	13.3%
Gender
Female	6,994,000	58.3%	76,146,000	58.1%
Male	4,984,000	41.5%	54,571,000	41.7%
Missing	26,000	0.2%	300,000	0.2%
Hospital demographic factors
Hospital region
Northeast	2,561,000	21.3%	27,650,000	21.1%
Midwest	3,007,000	25.1%	32,799,000	25.0%
South	3,878,000	32.3%	42,696,000	32.6%
West	2,557,000	21.3%	27,872,000	21.3%
Hospital location
Rural	1,507,000	12.6%	16,760,000	12.8%
Urban	10,348,000	86.2%	112,639,000	86.0%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital teaching status
Nonteaching	6,129,000	51.1%	67,447,000	51.5%
Teaching	5,726,000	47.7%	61,952,000	47.3%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital bed size
Small	1,496,000	12.5%	16,479,000	12.6%
Medium	2,905,000	24.2%	31,800,000	24.3%
Large	7,453,000	62.1%	81,120,000	61.9%
Missing	149,000	1.2%	1,617,000	1.2%
Admission severity factors
Admission source
Emergency department	1,078,000	9.0%	12,425,000	9.5%
Another hospital	110,000	0.9%	1,219,000	0.9%
Other health facility	61,000	0.5%	664,000	0.5%
Court/law enforcement	3,000	0.0%	35,000	0.0%
Routine	1,545,000	12.9%	16,529,000	12.6%
Missing	9,205,000	76.7%	100,144,000	76.4%
Admission type
Emergency	4,842,000	40.3%	53,386,000	40.7%
Urgent	1,985,000	16.5%	21,747,000	16.6%
Elective	2,570,000	21.4%	28,276,000	21.6%
Newborn	1,130,000	9.4%	11,625,000	8.9%
Trauma	57,000	0.5%	508,000	0.4%
Other	4,000	0.0%	45,000	0.0%
Missing	1,417,000	11.8%	15,430,000	11.8%
All‐Patient Refined DRG, severity
No class specified	10,000	0.1%	132,000	0.1%
Minor loss of function	4,289,000	35.7%	46,092,000	35.2%
Moderate loss of function	4,313,000	35.9%	47,150,000	36.0%
Major loss of function	2,630,000	21.9%	28,939,000	22.1%
Extreme loss of function	762,000	6.3%	8,704,000	6.6%

HAC Occurrence

Table 2.July Admission on Hospital‐Acquired Complication Occurrence Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: API, Asian/Pacific Islander; CI, confidence interval; DRG, DiagnosisRelated Group; OR, odds ratio.
Patient demographic factors
Admission time
July admit	1.06	1.061.07	<0.0001
Non‐July admit	Reference
Race
White	Reference
Black	0.78	0.750.80	<0.0001
Hispanic	0.81	0.760.85	<0.0001
API	0.75	0.710.80	<0.0001
Native American	0.92	0.831.02	0.1256
Other	0.91	0.840.98	<0.0001
Payer information
Medicare	1.00	0.971.02	0.7151
Medicaid	0.87	0.830.90	<0.0001
Private insurance	Reference
Selfpay	1.27	1.201.33	<0.0001
No charge	1.07	0.921.23	0.3871
Other	1.93	1.822.05	<0.0001
Comorbidities
No comorbidities	Reference
1	0.84	0.820.86	<0.0001
2 or more	0.70	0.680.72	<0.0001
Age category
18 years	0.35	0.330.37	<0.0001
1930 years	0.33	0.320.35	<0.0001
3040 years	0.32	0.310.33	<0.0001
4050 years	0.36	0.350.37	<0.0001
5065 years	0.37	0.360.38	<0.0001
6580 years	0.45	0.450.46	<0.0001
>80 years	Reference
Gender
Female	0.92	0.900.93	<0.0001
Male	Reference
Hospital demographic factors
Hospital region
Northeast	Reference
Midwest	1.06	1.001.13	0.2563
South	1.11	1.061.22	0.0005
West	1.08	0.971.20	0.1431
Hospital location
Rural	Reference
Urban	1.01	0.961.06	0.7144
Hospital teaching status
Nonteaching	Reference
Teaching	1.22	1.161.28	<0.0001
Hospital bed size
Small	Reference
Medium	1.06	1.001.13	0.0461
Large	1.11	1.061.17	0.0002
Admission severity factors
Admission source
Emergency department	1.63	1.481.80	<0.0001
Another hospital	1.96	1.762.17	<0.0001
Other health facility	1.62	1.302.03	<0.0001
Court/law enforcement	1.37	1.011.85	0.0438
Routine	Reference
Admission type
Emergency	2.15	2.032.28	<0.0001
Urgent	1.28	1.201.35	<0.0001
Elective	Reference
Newborn	0.69	0.630.76	<0.0001
Trauma	>1000	<0.001>1000	0.9962
Other	0.91	0.531.55	0.7183
AllPatient Refined DRG, severity
No class specified	0.73	0.620.85	<0.0001
Minor loss of function	Reference
Moderate loss of function	1.14	1.121.16	<0.0001
Major loss of function	1.61	1.571.66	<0.0001
Extreme loss of function	4.65	4.504.80	<0.0001

Higher Inpatient Charges and Prolonged LOS

Table 3.Higher Inpatient Costs and Prolonged LOS Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: CI, confidence interval; HAC, hospital‐acquired complication; LOS, length of stay; OR, odds ratio.
Higher inpatient costs
Admission time
July admit	1.00	0.991.01	0.9693
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.81	1.741.87	<0.0001
No HAC occurrence	Reference
Prolonged LOS
Admission time
July admit	0.98	0.980.99	<0.0001
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.45	1.421.48	<0.0001
No HAC occurrence	Reference

DISCUSSION

CONCLUSIONS

Disclosure

Nothing to report.

METHODS

Data Source

Patient Sample

Outcomes

Statistical Analyses

Statistical significance was achieved with a P value <0.05. All descriptive and logistic regression analyses were performed using SAS 9.4 (SAS Institute, Cary, NC).

RESULTS

Overview Demographics

Table 1.Population Demographics, 2008 to 2011
	July Admission, n=12,003,545		Non‐July Admission, n=131,015,837
	N	%	N	%
NOTE: Abbreviations: API, Asian/Pacific Islander; DRG, Diagnosis‐Related Group; HAC, hospital‐acquired complication. a Numbers are rounded to the 1,000th place.
Patient demographic factors
HAC occurrence
HAC during admission	594,000	4.9%	6,145,000	4.7%
No HAC during admission	11,410,000	95.1%	124,871,000	95.3%
Race
White	6,783,000	56.5%	74,222,000	56.7%
Black	1,468,000	12.2%	15,993,000	12.2%
Hispanic	1,231,000	10.3%	13,186,000	10.1%
API	288,000	2.4%	3,142,000	2.4%
Native American	77,000	0.6%	867,000	0.7%
Other	360,000	3.0%	3,931,000	3.0%
Missing	1,798,000	15.0%	19,675,000	15.0%
Payer information
Medicare	4,401,000	36.7%	49,209,000	37.6%
Medicaid	2,418,000	20.1%	25,977,000	19.8%
Private insurance	4,084,000	34.0%	44,106,000	33.7%
Self‐pay	636,000	5.3%	6,693,000	5.1%
No charge	43,000	0.4%	445,000	0.3%
Other	393,000	3.3%	4,261,000	3.3%
Missing	28,000	0.2%	323,000	0.2%
Comorbidities
No comorbidities	3,957,000	33.0%	42,249,000	32.2%
1	2,104,000	17.5%	23,209,000	17.7%
2 or more	5,943,000	49.5%	65,557,000	50.0%
Age category
18 years	1,965,000	16.4%	21,702,000	16.6%
1930 years	1,482,000	12.3%	15,385,000	11.7%
3040 years	1,156,000	9.6%	12,091,000	9.2%
4050 years	1,196,000	10.0%	12,737,000	9.7%
5065 years	2,323,000	19.4%	25,458,000	19.4%
6580 years	2,345,000	19.5%	26,218,000	20.0%
>80 years	1,536,000	12.8%	17,424,000	13.3%
Gender
Female	6,994,000	58.3%	76,146,000	58.1%
Male	4,984,000	41.5%	54,571,000	41.7%
Missing	26,000	0.2%	300,000	0.2%
Hospital demographic factors
Hospital region
Northeast	2,561,000	21.3%	27,650,000	21.1%
Midwest	3,007,000	25.1%	32,799,000	25.0%
South	3,878,000	32.3%	42,696,000	32.6%
West	2,557,000	21.3%	27,872,000	21.3%
Hospital location
Rural	1,507,000	12.6%	16,760,000	12.8%
Urban	10,348,000	86.2%	112,639,000	86.0%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital teaching status
Nonteaching	6,129,000	51.1%	67,447,000	51.5%
Teaching	5,726,000	47.7%	61,952,000	47.3%
Missing	149,000	1.2%	1,617,000	1.2%
Hospital bed size
Small	1,496,000	12.5%	16,479,000	12.6%
Medium	2,905,000	24.2%	31,800,000	24.3%
Large	7,453,000	62.1%	81,120,000	61.9%
Missing	149,000	1.2%	1,617,000	1.2%
Admission severity factors
Admission source
Emergency department	1,078,000	9.0%	12,425,000	9.5%
Another hospital	110,000	0.9%	1,219,000	0.9%
Other health facility	61,000	0.5%	664,000	0.5%
Court/law enforcement	3,000	0.0%	35,000	0.0%
Routine	1,545,000	12.9%	16,529,000	12.6%
Missing	9,205,000	76.7%	100,144,000	76.4%
Admission type
Emergency	4,842,000	40.3%	53,386,000	40.7%
Urgent	1,985,000	16.5%	21,747,000	16.6%
Elective	2,570,000	21.4%	28,276,000	21.6%
Newborn	1,130,000	9.4%	11,625,000	8.9%
Trauma	57,000	0.5%	508,000	0.4%
Other	4,000	0.0%	45,000	0.0%
Missing	1,417,000	11.8%	15,430,000	11.8%
All‐Patient Refined DRG, severity
No class specified	10,000	0.1%	132,000	0.1%
Minor loss of function	4,289,000	35.7%	46,092,000	35.2%
Moderate loss of function	4,313,000	35.9%	47,150,000	36.0%
Major loss of function	2,630,000	21.9%	28,939,000	22.1%
Extreme loss of function	762,000	6.3%	8,704,000	6.6%

HAC Occurrence

Table 2.July Admission on Hospital‐Acquired Complication Occurrence Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: API, Asian/Pacific Islander; CI, confidence interval; DRG, DiagnosisRelated Group; OR, odds ratio.
Patient demographic factors
Admission time
July admit	1.06	1.061.07	<0.0001
Non‐July admit	Reference
Race
White	Reference
Black	0.78	0.750.80	<0.0001
Hispanic	0.81	0.760.85	<0.0001
API	0.75	0.710.80	<0.0001
Native American	0.92	0.831.02	0.1256
Other	0.91	0.840.98	<0.0001
Payer information
Medicare	1.00	0.971.02	0.7151
Medicaid	0.87	0.830.90	<0.0001
Private insurance	Reference
Selfpay	1.27	1.201.33	<0.0001
No charge	1.07	0.921.23	0.3871
Other	1.93	1.822.05	<0.0001
Comorbidities
No comorbidities	Reference
1	0.84	0.820.86	<0.0001
2 or more	0.70	0.680.72	<0.0001
Age category
18 years	0.35	0.330.37	<0.0001
1930 years	0.33	0.320.35	<0.0001
3040 years	0.32	0.310.33	<0.0001
4050 years	0.36	0.350.37	<0.0001
5065 years	0.37	0.360.38	<0.0001
6580 years	0.45	0.450.46	<0.0001
>80 years	Reference
Gender
Female	0.92	0.900.93	<0.0001
Male	Reference
Hospital demographic factors
Hospital region
Northeast	Reference
Midwest	1.06	1.001.13	0.2563
South	1.11	1.061.22	0.0005
West	1.08	0.971.20	0.1431
Hospital location
Rural	Reference
Urban	1.01	0.961.06	0.7144
Hospital teaching status
Nonteaching	Reference
Teaching	1.22	1.161.28	<0.0001
Hospital bed size
Small	Reference
Medium	1.06	1.001.13	0.0461
Large	1.11	1.061.17	0.0002
Admission severity factors
Admission source
Emergency department	1.63	1.481.80	<0.0001
Another hospital	1.96	1.762.17	<0.0001
Other health facility	1.62	1.302.03	<0.0001
Court/law enforcement	1.37	1.011.85	0.0438
Routine	Reference
Admission type
Emergency	2.15	2.032.28	<0.0001
Urgent	1.28	1.201.35	<0.0001
Elective	Reference
Newborn	0.69	0.630.76	<0.0001
Trauma	>1000	<0.001>1000	0.9962
Other	0.91	0.531.55	0.7183
AllPatient Refined DRG, severity
No class specified	0.73	0.620.85	<0.0001
Minor loss of function	Reference
Moderate loss of function	1.14	1.121.16	<0.0001
Major loss of function	1.61	1.571.66	<0.0001
Extreme loss of function	4.65	4.504.80	<0.0001

Higher Inpatient Charges and Prolonged LOS

Table 3.Higher Inpatient Costs and Prolonged LOS Multivariable Analysis
	OR	95% CI	P Value
NOTE: Abbreviations: CI, confidence interval; HAC, hospital‐acquired complication; LOS, length of stay; OR, odds ratio.
Higher inpatient costs
Admission time
July admit	1.00	0.991.01	0.9693
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.81	1.741.87	<0.0001
No HAC occurrence	Reference
Prolonged LOS
Admission time
July admit	0.98	0.980.99	<0.0001
Non‐July admit	Reference
HAC occurrence
HAC occurrence	1.45	1.421.48	<0.0001
No HAC occurrence	Reference

DISCUSSION

CONCLUSIONS

Disclosure

Nothing to report.

References

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Walraven C, Jennings A, Wong J, Forster AJ. Influence of house‐staff experience on teaching‐hospital mortality: the “July phenomenon” revisited. J Hosp Med. 2011;6(7):389–394.
McDonald RJ, Cloft HJ, Kallmes DF. Impact of admission month and hospital teaching status on outcomes in subarachnoid hemorrhage: evidence against the July effect. J Neurosurg. 2012;116(1):157–163.
Morse BC, Boland BN, Blackhurst DW, Roettger RH. Analysis of Centers for Medicaid and Medicare Services ‘never events’ in elderly patients undergoing bowel operations. Am Surg. 2010;76(8):841–845.
Milstein A. Ending extra payment for “never events”—stronger incentives for patients' safety. N Engl J Med. 2009;360(23):2388–2390.
Rosenthal MB. Nonpayment for performance? Medicare's new reimbursement rule. N Engl J Med. 2007;357(16):1573–1575.
Wen T, He S, Attenello F, et al. The impact of patient age and comorbidities on the occurrence of “never events” in cerebrovascular surgery: an analysis of the Nationwide Inpatient Sample. J Neurosurg. 2014;121(3):580–586.
Joice GA, Deibert CM, Kates M, Spencer BA, McKiernan JM. “Never events”: Centers for Medicare and Medicaid Services complications after radical cystectomy. Urology. 2013;81(3):527–532.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. Available at: http://www.ahrq.gov/data/hcup/index.html. Accessed June 2013.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. HCUP frequently asked questions. Available at: www.hcup‐us.ahrq.gov/tech_assist/faq.jsp. Accessed January 2015.
US Department of Health and Human Services. Centers for Medicare 290(14):1868–1874.
Hamdy O, Ernst FR, Baumer D, Mustad V, Partridge J, Hegazi R. Differences in resource utilization between patients with diabetes receiving glycemia‐targeted specialized nutrition vs standard nutrition formulas in U.S. hospitals. JPEN J Parenter Enteral Nutr. 2014;38(2 suppl):86S–91S.
Stanford R, McLaughlin T, Okamoto LJ. The cost of asthma in the emergency department and hospital. Am J Respir Crit Care Med. 1999;160(1):211–215.
Mullen MT, Kasner SE, Kallan MJ, Kleindorfer DO, Albright KC, Carr BG. Joint Commission primary stroke centers utilize more rt‐PA in the Nationwide Inpatient Sample. J Am Heart Assoc. 2013;2(2):e000071.
Berglund PA. An introduction to multiple imputation of complex sample data using SAS 9.2. Paper presented at: SAS Global Forum Proceedings, April 11–14, 2010; Seattle, WA.
Blumberg MS. Measuring surgical quality in Maryland: a model. Health Aff. 1988;7(1):62–78.
Englesbe MJ, Pelletier SJ, Magee JC, et al. Seasonal variation in surgical outcomes as measured by the American College of Surgeons‐National Surgical Quality Improvement Program (ACS‐NSQIP). Ann Surg. 2007;246(3):456–462; discussion 463–465.
Inaba K, Recinos G, Teixeira PG, et al. Complications and death at the start of the new academic year: is there a July phenomenon? J Trauma. 2010;68(1):19–22.
Bakaeen FG, Huh J, LeMaire SA, et al. The July effect: impact of the beginning of the academic cycle on cardiac surgical outcomes in a cohort of 70,616 patients. Ann Thorac Surg. 2009;88(1):70–75.
Ford AA, Bateman BT, Simpson LL, Ratan RB. Nationwide data confirms absence of 'July phenomenon' in obstetrics: it's safe to deliver in July. J Perinatol. 2007;27(2):73–76.
Barry WA, Rosenthal GE. Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals. J Gen Intern Med. 2003;18(8):639–645.
Weaver KJ, Neal D, Hoh DJ, Mocco J, Barker FG, Hoh BL. The “July phenomenon” for neurosurgical mortality and complications in teaching hospitals: an analysis of more than 850,000 neurosurgical patients in the nationwide inpatient sample database, 1998 to 2008. Neurosurgery. 2012;71(3):562–571; discussion 571.
Anderson KL, Koval KJ, Spratt KF. Hip fracture outcome: is there a “July effect.” Am J Orthop. 2009;38(12):606–611.
Shuhaiber JH, Goldsmith K, Nashef SA. Impact of cardiothoracic resident turnover on mortality after cardiac surgery: a dynamic human factor. Ann Thorac Surg. 2008;86(1):123–131.
Phillips DP, Barker GE. A July spike in fatal medication errors: a possible effect of new medical residents. J Gen Intern Med. 2010;25(8):774–779.
Gosbee J. Human factors engineering and patient safety. Qual Saf Health Care. 2002;11(4):352–354.
Walling HW, Veremakis C. Ordering errors by first‐year residents: evidence of learning from mistakes. Mo Med. 2004;101(2):128–131.
Goldsack J, Bergey M, Mascioli S, Cunningham J. Hourly rounding and patient falls: What factors boost success? Nursing. 2015;45(2):25–30.
McDonald JS, Clarke MJ, Helm GA, Kallmes DF. The effect of July admission on inpatient outcomes following spinal surgery: clinical article. J Neurosurg Spine. 2013;18(3):280–288.
Dhaliwal AS, Chu D, Deswal A, et al. The July effect and cardiac surgery: the effect of the beginning of the academic cycle on outcomes. Am J Surg. 2008;196(5):720–725.
Bohl DD, Fu MC, Gruskay JA, Basques BA, Golinvaux NS, Grauer JN. “July Effect” in elective spine surgery: analysis of the American College of Surgeons National Surgical Quality Improvement Program Database. Spine. 2014;39(7):603–611.
Shulkin DJ. The July phenomenon revisited: are hospital complications associated with new house staff? Am J Med Qual. 1995;10(1):14–17.
Rich EC, Hillson SD, Dowd B, Morris N. Specialty differences in the 'July Phenomenon' for Twin Cities teaching hospitals. Med Care. 1993;31(1):73–83.
Teufack SG, Campbell P, Jabbour P, Maltenfort M, Evans J, Ratliff JK. Potential financial impact of restriction in “never event” and periprocedural hospital‐acquired condition reimbursement at a tertiary neurosurgical center: a single‐institution prospective study. J neurosurg. 2010;112(2):249–256.
Allman RM, Goode PS, Burst N, Bartolucci AA, Thomas DR. Pressure ulcers, hospital complications, and disease severity: impact on hospital costs and length of stay. Adv Wound Care. 1999;12(1):22–30.
Fleischmann KE, Goldman L, Young B, Lee TH. Association between cardiac and noncardiac complications in patients undergoing noncardiac surgery: outcomes and effects on length of stay. Am J Med. 2003;115(7):515–520.
Lee MK, Dodson TB, Karimbux NY, Nalliah RP, Allareddy V. Effect of occurrence of infection‐related never events on length of stay and hospital charges in patients undergoing radical neck dissection for head and neck cancer. Oral Surg Oral Med Oral Pathol Oral Radiol. 2013;116(2):147–158.
Stansbury JP, Jia H, Williams LS, Vogel WB, Duncan PW. Ethnic disparities in stroke: epidemiology, acute care, and postacute outcomes. Stroke. 2005;36(2):374–386.
Whitehouse JD, Friedman ND, Kirkland KB, Richardson WJ, Sexton DJ. The impact of surgical‐site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol. 2002;23(4):183–189.
Burns EM, Rigby E, Mamidanna R, et al. Systematic review of discharge coding accuracy. J Public Health (Oxf). 2012;34(1):138–148.
Ghosh S, Pahwa P. Assessing bias associated with missing data from joint Canada/US survey of health: an application. Biometrics Section JSM. 2008:3394–3401.
Wagner AK, Soumerai SB, Zhang F, Ross‐Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309.

References

Haller G, Myles PS, Taffe P, Perneger TV, Wu CL. Rate of undesirable events at beginning of academic year: retrospective cohort study. BMJ. 2009;339:b3974.
Barzansky B, Etzel SI. Medical schools in the United States, 2007–2008. JAMA. 2008;300(10):1221–1227.
Young JQ, Ranji SR, Wachter RM, Lee CM, Niehaus B, Auerbach AD. “July effect”: impact of the academic year‐end changeover on patient outcomes: a systematic review. Ann Intern Med. 2011;155(5):309–315.
Walraven C, Jennings A, Wong J, Forster AJ. Influence of house‐staff experience on teaching‐hospital mortality: the “July phenomenon” revisited. J Hosp Med. 2011;6(7):389–394.
McDonald RJ, Cloft HJ, Kallmes DF. Impact of admission month and hospital teaching status on outcomes in subarachnoid hemorrhage: evidence against the July effect. J Neurosurg. 2012;116(1):157–163.
Morse BC, Boland BN, Blackhurst DW, Roettger RH. Analysis of Centers for Medicaid and Medicare Services ‘never events’ in elderly patients undergoing bowel operations. Am Surg. 2010;76(8):841–845.
Milstein A. Ending extra payment for “never events”—stronger incentives for patients' safety. N Engl J Med. 2009;360(23):2388–2390.
Rosenthal MB. Nonpayment for performance? Medicare's new reimbursement rule. N Engl J Med. 2007;357(16):1573–1575.
Wen T, He S, Attenello F, et al. The impact of patient age and comorbidities on the occurrence of “never events” in cerebrovascular surgery: an analysis of the Nationwide Inpatient Sample. J Neurosurg. 2014;121(3):580–586.
Joice GA, Deibert CM, Kates M, Spencer BA, McKiernan JM. “Never events”: Centers for Medicare and Medicaid Services complications after radical cystectomy. Urology. 2013;81(3):527–532.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. Available at: http://www.ahrq.gov/data/hcup/index.html. Accessed June 2013.
Agency for Healthcare Research and Quality. Healthcare Cost and Utilization Project. HCUP frequently asked questions. Available at: www.hcup‐us.ahrq.gov/tech_assist/faq.jsp. Accessed January 2015.
US Department of Health and Human Services. Centers for Medicare 290(14):1868–1874.
Hamdy O, Ernst FR, Baumer D, Mustad V, Partridge J, Hegazi R. Differences in resource utilization between patients with diabetes receiving glycemia‐targeted specialized nutrition vs standard nutrition formulas in U.S. hospitals. JPEN J Parenter Enteral Nutr. 2014;38(2 suppl):86S–91S.
Stanford R, McLaughlin T, Okamoto LJ. The cost of asthma in the emergency department and hospital. Am J Respir Crit Care Med. 1999;160(1):211–215.
Mullen MT, Kasner SE, Kallan MJ, Kleindorfer DO, Albright KC, Carr BG. Joint Commission primary stroke centers utilize more rt‐PA in the Nationwide Inpatient Sample. J Am Heart Assoc. 2013;2(2):e000071.
Berglund PA. An introduction to multiple imputation of complex sample data using SAS 9.2. Paper presented at: SAS Global Forum Proceedings, April 11–14, 2010; Seattle, WA.
Blumberg MS. Measuring surgical quality in Maryland: a model. Health Aff. 1988;7(1):62–78.
Englesbe MJ, Pelletier SJ, Magee JC, et al. Seasonal variation in surgical outcomes as measured by the American College of Surgeons‐National Surgical Quality Improvement Program (ACS‐NSQIP). Ann Surg. 2007;246(3):456–462; discussion 463–465.
Inaba K, Recinos G, Teixeira PG, et al. Complications and death at the start of the new academic year: is there a July phenomenon? J Trauma. 2010;68(1):19–22.
Bakaeen FG, Huh J, LeMaire SA, et al. The July effect: impact of the beginning of the academic cycle on cardiac surgical outcomes in a cohort of 70,616 patients. Ann Thorac Surg. 2009;88(1):70–75.
Ford AA, Bateman BT, Simpson LL, Ratan RB. Nationwide data confirms absence of 'July phenomenon' in obstetrics: it's safe to deliver in July. J Perinatol. 2007;27(2):73–76.
Barry WA, Rosenthal GE. Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals. J Gen Intern Med. 2003;18(8):639–645.
Weaver KJ, Neal D, Hoh DJ, Mocco J, Barker FG, Hoh BL. The “July phenomenon” for neurosurgical mortality and complications in teaching hospitals: an analysis of more than 850,000 neurosurgical patients in the nationwide inpatient sample database, 1998 to 2008. Neurosurgery. 2012;71(3):562–571; discussion 571.
Anderson KL, Koval KJ, Spratt KF. Hip fracture outcome: is there a “July effect.” Am J Orthop. 2009;38(12):606–611.
Shuhaiber JH, Goldsmith K, Nashef SA. Impact of cardiothoracic resident turnover on mortality after cardiac surgery: a dynamic human factor. Ann Thorac Surg. 2008;86(1):123–131.
Phillips DP, Barker GE. A July spike in fatal medication errors: a possible effect of new medical residents. J Gen Intern Med. 2010;25(8):774–779.
Gosbee J. Human factors engineering and patient safety. Qual Saf Health Care. 2002;11(4):352–354.
Walling HW, Veremakis C. Ordering errors by first‐year residents: evidence of learning from mistakes. Mo Med. 2004;101(2):128–131.
Goldsack J, Bergey M, Mascioli S, Cunningham J. Hourly rounding and patient falls: What factors boost success? Nursing. 2015;45(2):25–30.
McDonald JS, Clarke MJ, Helm GA, Kallmes DF. The effect of July admission on inpatient outcomes following spinal surgery: clinical article. J Neurosurg Spine. 2013;18(3):280–288.
Dhaliwal AS, Chu D, Deswal A, et al. The July effect and cardiac surgery: the effect of the beginning of the academic cycle on outcomes. Am J Surg. 2008;196(5):720–725.
Bohl DD, Fu MC, Gruskay JA, Basques BA, Golinvaux NS, Grauer JN. “July Effect” in elective spine surgery: analysis of the American College of Surgeons National Surgical Quality Improvement Program Database. Spine. 2014;39(7):603–611.
Shulkin DJ. The July phenomenon revisited: are hospital complications associated with new house staff? Am J Med Qual. 1995;10(1):14–17.
Rich EC, Hillson SD, Dowd B, Morris N. Specialty differences in the 'July Phenomenon' for Twin Cities teaching hospitals. Med Care. 1993;31(1):73–83.
Teufack SG, Campbell P, Jabbour P, Maltenfort M, Evans J, Ratliff JK. Potential financial impact of restriction in “never event” and periprocedural hospital‐acquired condition reimbursement at a tertiary neurosurgical center: a single‐institution prospective study. J neurosurg. 2010;112(2):249–256.
Allman RM, Goode PS, Burst N, Bartolucci AA, Thomas DR. Pressure ulcers, hospital complications, and disease severity: impact on hospital costs and length of stay. Adv Wound Care. 1999;12(1):22–30.
Fleischmann KE, Goldman L, Young B, Lee TH. Association between cardiac and noncardiac complications in patients undergoing noncardiac surgery: outcomes and effects on length of stay. Am J Med. 2003;115(7):515–520.
Lee MK, Dodson TB, Karimbux NY, Nalliah RP, Allareddy V. Effect of occurrence of infection‐related never events on length of stay and hospital charges in patients undergoing radical neck dissection for head and neck cancer. Oral Surg Oral Med Oral Pathol Oral Radiol. 2013;116(2):147–158.
Stansbury JP, Jia H, Williams LS, Vogel WB, Duncan PW. Ethnic disparities in stroke: epidemiology, acute care, and postacute outcomes. Stroke. 2005;36(2):374–386.
Whitehouse JD, Friedman ND, Kirkland KB, Richardson WJ, Sexton DJ. The impact of surgical‐site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol. 2002;23(4):183–189.
Burns EM, Rigby E, Mamidanna R, et al. Systematic review of discharge coding accuracy. J Public Health (Oxf). 2012;34(1):138–148.
Ghosh S, Pahwa P. Assessing bias associated with missing data from joint Canada/US survey of health: an application. Biometrics Section JSM. 2008:3394–3401.
Wagner AK, Soumerai SB, Zhang F, Ross‐Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299–309.

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Journal of Hospital Medicine - 10(7)

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The july effect: An analysis of never events in the nationwide inpatient sample

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The july effect: An analysis of never events in the nationwide inpatient sample

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Timothy Wen, MPH

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Address for correspondence and reprint requests: Timothy Wen, MPH, Keck School of Medicine of USC, University of Southern California, c/o: Dr. William Mack, 1520 San Pablo Street, Suite 3800, Los Angeles, CA 90033; E‐mail: [email protected]

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In reference to “The effect of a rapid response team on resident perceptions of education and autonomy”

Author(s)

Nancy H Stewart, DO

Audrey Tanksley, MD

Vineet M Arora, MD, MAPP

We read with great interest the study by Butcher and colleagues[1] on resident perceptions of rapid response teams (RRTs) with regard to education and autonomy. We found it interesting to note that one‐third of residents felt the nurse should always notify the primary resident when calling an RRT. Nursing literature demonstrates that ambivalence exists on when to notify the physician,[2] thus suggesting nurse‐physician interactions are still suboptimal and an area for future improvement. Given the focus on interprofessional training and practice by both the Accreditation Council of Graduate Medical Education and Liaison Committee on Medical Education,[3, 4] RRTs provide a perfect opportunity to improve interprofessional training and practice through better physician‐nurse collaboration.

Interestingly, the future of RRT activation can also be streamlined to avoid nurse‐physician conflicts about who should be notified. For example, the technology exists for automated alerts in the electronic medical record to trigger when a patient decompensates,[5] thereby activating an RRT. One can imagine this technology circumvents the physician and nurse when initiating the RRT. Given the potential uses of such technology, future studies regarding physician autonomy with automatic triggering of an RRT will be equally valuable.

References

Butcher BW, Quist CE, Harrison JD, Ranji SR. The effect of a rapid response team on resident perceptions of education and autonomy. J Hosp Med. 2015;10(1):8–12.
Astroth KS, Woith WM, Stapleton SJ, Degitz RJ, Jenkins SH. Qualitative exploration of nurses' decisions to activate rapid response teams. J Clin Nurs. 2013;22(19‐20):2876–2882.
Iobst W, Aagaard E, Bazari H, et al.; Internal Medicine Milestone Group. The Internal Medicine Milestone Project. The Accreditation Council for Graduate Medical Education and The American Board of Internal Medicine. Available at: https://www.acgme.org/acgmeweb/Portals/0/PDFs/Milestones/InternalMedicineMilestones.pdf. Accessed February 12, 2015.
Liaison Committee on Medical Education. 2013 summary of new and revised LCME accreditation standards and annotations. Available at: http://www.lcme.org/2013‐new‐and_revised‐standards‐summary.pdf. Accessed February 12, 2015.
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs. Crit Care Med. 2012;40(7):2102–2108.

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Nancy H Stewart, DO

Audrey Tanksley, MD

Vineet M Arora, MD, MAPP

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Nancy H Stewart, DO

Audrey Tanksley, MD

Vineet M Arora, MD, MAPP

Article PDF

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References

Butcher BW, Quist CE, Harrison JD, Ranji SR. The effect of a rapid response team on resident perceptions of education and autonomy. J Hosp Med. 2015;10(1):8–12.
Astroth KS, Woith WM, Stapleton SJ, Degitz RJ, Jenkins SH. Qualitative exploration of nurses' decisions to activate rapid response teams. J Clin Nurs. 2013;22(19‐20):2876–2882.
Iobst W, Aagaard E, Bazari H, et al.; Internal Medicine Milestone Group. The Internal Medicine Milestone Project. The Accreditation Council for Graduate Medical Education and The American Board of Internal Medicine. Available at: https://www.acgme.org/acgmeweb/Portals/0/PDFs/Milestones/InternalMedicineMilestones.pdf. Accessed February 12, 2015.
Liaison Committee on Medical Education. 2013 summary of new and revised LCME accreditation standards and annotations. Available at: http://www.lcme.org/2013‐new‐and_revised‐standards‐summary.pdf. Accessed February 12, 2015.
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs. Crit Care Med. 2012;40(7):2102–2108.

References

Butcher BW, Quist CE, Harrison JD, Ranji SR. The effect of a rapid response team on resident perceptions of education and autonomy. J Hosp Med. 2015;10(1):8–12.
Astroth KS, Woith WM, Stapleton SJ, Degitz RJ, Jenkins SH. Qualitative exploration of nurses' decisions to activate rapid response teams. J Clin Nurs. 2013;22(19‐20):2876–2882.
Iobst W, Aagaard E, Bazari H, et al.; Internal Medicine Milestone Group. The Internal Medicine Milestone Project. The Accreditation Council for Graduate Medical Education and The American Board of Internal Medicine. Available at: https://www.acgme.org/acgmeweb/Portals/0/PDFs/Milestones/InternalMedicineMilestones.pdf. Accessed February 12, 2015.
Liaison Committee on Medical Education. 2013 summary of new and revised LCME accreditation standards and annotations. Available at: http://www.lcme.org/2013‐new‐and_revised‐standards‐summary.pdf. Accessed February 12, 2015.
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs. Crit Care Med. 2012;40(7):2102–2108.

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Journal of Hospital Medicine - 10(6)

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Journal of Hospital Medicine - 10(6)

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In reference to “The effect of a rapid response team on resident perceptions of education and autonomy”

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A vaginoscopic approach to diagnostic hysteroscopy

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A vaginoscopic approach to diagnostic hysteroscopy

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Amy L. Garcia, MD

In this video, diffuse complex endometrial hyperplasia is identified using a vaginoscopic approach with a 1.9-mm diagnostic rigid hysteroscope. The posterior fornix of the vagina is filled with saline until the cervix is elevated with the fluid and the cervical os is identified. The cervical canal is entered and gentle rotational movement and hydrodistension allows the canal to be traversed into the uterine cavity.

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Dr. Garcia is Director, Center for Women’s Surgery and Garcia Institute for Hysteroscopic Training, Albuquerque, and Clinical Assistant Professor, Department of Obstetrics and Gynecology, University of New Mexico School of Medicine, Albuquerque. Dr. Garcia serves on the OBG Management Board of Editors.

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Amy L. Garcia MD, Malcolm Munro MD, Update on minimally invasive gynecology, in-office hysteroscopy, patient anxiety, patient pain, office hysteroscopy, low patient tolerance of discomfort, hysteroscopy without anesthesia, diagnostic hysteroscopy, vaginoscopic approach, “no-touch” technique, polyps, flexible lenses, chronic pelvic pain, cesarean delivery, preprocedural paracervical block, local anesthetic, cervical pain, preprocedural cyclooxygenase inhibitors, COX inhibitors, ibuprofen, sodium naproxen, lidocaine, onset of action, calm and relaxing environment, music, State-Trait Anxiety Inventory, STAI, visual analog scale, protocol for pain relief, dysmenorrhea, dyspareunia,

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Consent to treat minors: a major complexity

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Consent to treat minors: a major complexity

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Francine Pearce, MD

The relationship between parents and pediatricians is unique. More than any other field of medicine, there is a level of trust that develops because of the consistent and ongoing interaction for several years. But as the child grows older and enters the adolescent years, the relationship shifts from catering to the desires of the parent to the needs of the child.

When a strong relationship is established, the transition of trust is usually easy, and parents are very comfortable and welcoming of an independent relationship between the physician and the child. But there are many issues that come up in adolescence that may be very difficult for a child to discuss with the parent despite having a good relationship, putting the physician directly in the middle.

The issue of minor consent is complex, and because it differs from state to state, it becomes even more complex. Of course, the best approach is to have a conversation with the parent to determine their views on various issues and ask for consent to address them should their child present to you for treatment, but new patients present, and time to establish a relationship is not always possible. The American Academy of Pediatrics statement on treatment of adolescents requires that every attempt is made to encourage inclusion of the parent in any decision making.

Understanding the laws that govern the state in which you practice is imperative. The state policies and laws can be found at www.Guttmacher.org. Although there has not been a physician held liable for nonnegligent care given to a minor who gave consent, it is important for parents to understand what their child can consent to or against. It also is important for the physician to be explicitly clear as to what their limitations are by law.

A minor status is defined by age under 18 years of age. An emancipated minor is someone who attained legal adulthood because of marriage, military service, or living separately from parents and managing one’s financial affairs (Understanding Legal Aspects of Care, in “Adolescent Health Care: A Practical Guide,” 5th ed [Philadelphia Lippincott Williams & Wilkins, 2008]). These laws are very clear and do not usually cause much confusion. Where the situation becomes very grey is in the case of the mature minor. This category is recognized in some states as an exception to the rules requiring parental consent for medical care (Int. J. Gynaecol. Obstet. 1998;63:295-300). The mature minor is defined as being at least 14 years old, having the ability to understand risk and benefits, and having the ability to provide informed consent. But this requires a subjective assessment of the adolescent, which could be argued by the parent.

Minors can consent to contraceptive services in most states. In 1977, the Supreme Court ruled that the right to privacy protects a minor’s access to nonprescriptive contraception, and although prescribed contraception is not included, it is generally considered to be included (Med. Clin. North Am, 1990; 74:1097-112). It is important to note that a pharmacist under the Pharmacist Conscience Clause, in some states, can refuse to fill the prescription without parental consent at their discretion (Arch. Pediatr. Adolesc. Med. 2003;157:361-5). Although this not a common issue, it may present a larger issue if the patient requested confidentiality.

Diagnosis and treatment of sexually transmitted disease also can be done with the consent of a minor, but the age of the patient, usually greater than 14 years, is required in most states. A careful assessment must be done for abuse regardless of whether the minor admitted to consensual sex or not. The laws regarding statutory rape are clearly defined state to state and may present a larger problem if disputed by the parent.

Elective abortion is always a topic of debate. States require at least one parent to consent when a minor is seeking an abortion, but a minor also can seek a judicial bypass, which is a request from a minor to not have parental consent for an abortion if they believe that notification will bring harm to the minor. Conversely, an adolescent also can refuse to consent to an abortion that the parent requests.

Immunizations also can be given with the consent of the minor, but extra precaution should be given to documentation of clear explanation of risk and benefits. Despite there being no federal law requiring parental consent, some states do require it, and it is prudent to obtain it.

Parents don’t often realize the limitations of their ability to prevent or demand treatment. So although the abortion itself falls outside the scope of care of a pediatrician, educating parents on the laws can help them navigate the situation better. Parents also may request drug, sexually transmitted infection, or pregnancy testing without the knowledge of the minor. Whether it is done is left to the discretion of the physician but the AAP advises that this only be done as a rare exception (Pediatrics 2007;119:627-30).

Now a larger consideration for physicians is financial liability. Parents are not obligated to pay for treatment and procedures for which they did not consent. The financial responsibility falls on the minor who requested it. Obviously, this could be costly for the facility, and therefore a decision has to be made to either disrupt continuity of care and refer to an outside facility or absorb the cost. This can be a challenging decision. Disclosing to the minor that payment sent through the insurance might unintentionally breach the confidentiality of the treatment is also an important consideration if the minor’s desire is to keep the parent uninformed.

The issue of consent to treatment when it comes to minors is multifaceted. Maintaining the trust of the parent and gaining the trust of the adolescent is tricky when the lines of communication between them are limited. Establishing early a relationship of trust with the parent to advise and treat the child appropriately in the event he or she does present with complex issues will settle many of the issues. More importantly, as pediatricians our goal is to establish a relationship with the adolescent so that he or she knows where to go to get good sound advice and treatment to ensure good health and prevent avoidable consequences.

Dr. Pearce is a pediatrician in Frankfort, Ill. E-mail her at [email protected]. Scan this QR code to view similar columns or go to pediatricnews.com.

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Day Two highlights from HM15, the Society of Hospital Medicine’s (SHM) annual meeting in National Harbor, Md., just outside Washington, D.C.

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Day Two highlights from HM15, the Society of Hospital Medicine’s (SHM) annual meeting in National Harbor, Md., just outside Washington, D.C.

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Day Two highlights from HM15, the Society of Hospital Medicine’s (SHM) annual meeting in National Harbor, Md., just outside Washington, D.C.

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The ABIM sends a follow-up letter

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A month ago the American Board of Internal Medicine sent a letter to diplomates saying they “got it wrong,” referring to the process of maintenance of certification or MOC, that the board required. They acknowledged that “parts of the new program are not meeting the needs of physicians like yourself.” Some of the things they proposed include changing the content of the Internal Medicine recertification exam to be “more reflective of what physicians in practice are doing,” with a promise that subspecialty recertification exams will follow suit. They also talk about “new and more flexible ways ... to demonstrate ... medical knowledge,” likely making room for the continuing medical education or CME credits that state licensures require.

While the letter was evidence that physician grievances were being heard, it was widely criticized for not having gone far enough. Questions remained about the financial and time cost of certification, the relevance of the exam, and the motivation of the board.

Well, the ABIM has written us again. Except it’s still not saying much. They simply say that they have been listening to feedback, and they list some points about what they’ve been hearing and are presumably going to take into consideration. In summary, they are recognizing that the while we all agree that we need a way for physicians to keep up on their medical knowledge, there is “a shared sense that defining ‘keeping up’ is the work of the whole community, including physicians, specialty societies, patient groups, and health care institutions.”

They proceed to outline what they’ve heard and will presumably consider, including the suggestion that the recertification exam be eliminated completely, and that CME units count toward recertification. Like I said, they didn’t say much. But this is promising. Particularly telling is the part where they acknowledge that the job of defining ‘keeping up’ does not fall solely on, as a friend put it, people that sit in their ivory towers and are removed from the daily grind of patient care.

Apropos of all this, I recently got my first 10 points toward MOC by taking a 30-question exam posted by the American College of Rheumatology. Each question comes with a list of references that you can review should you need or want to. To my great surprise, one of the references was in German. So what does that tell you about the people writing the questions and the process they use? What does that tell you about the validity of the questions as a measure of my competence and ability to treat people?

Exams like the boards are a measure of test-taking skills and retention, neither one of which is a true measure of what a capable, competent physician should be. The ABIM is imposing on us an onerous and ill-conceived tool, one that most physicians agree is irrelevant. I am glad this conversation is happening, because frankly the process was enough to make me want to quit being a doctor.

Dr. Chan practices rheumatology in Pawtucket, R.I.

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LEGACY: Weight loss markedly improves atrial fibrillation

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SAN DIEGO – Maintenance of long-term weight loss in obese or overweight individuals with atrial fibrillation was associated with nearly a sixfold greater likelihood of freedom from recurrent AF during nearly 5 years of active follow-up in the LEGACY study.

“The effect was dose dependent. The most important finding is that 46% of patients with at least a 10% weight loss were free from AF without the use of drugs or ablation procedures through nearly 5 years, versus 22% of those with 3%-9% weight loss, and just 13% with less than a 3% weight loss,” Dr. Rajeev K. Pathak said at the annual meeting of the American College of Cardiology.

LEGACY (Long-Term Effect of Goal-Directed Weight Management in an Atrial Fibrillation Cohort: A Long-Term Follow-Up Study) included 355 overweight or obese participants with paroxsymal or persistent AF who were offered the chance to participate in a dedicated weight-loss clinic. Regular participation in this clinic proved to be a key factor in losing weight, keeping it off, and reducing AF burden; the more frequently patients attended the quarterly sessions the better the outcomes, noted Dr. Pathak, a cardiologist and electrophysiology fellow at the University of Adelaide (Australia).

Of the 355 participants, 135 lost at least 10% of their body weight, 103 had a 3%-9% drop in body weight, and 117 had less than a 3% loss or a weight gain.

Year-by-year weight trends had a significant impact on outcome. The 141 patients with linear weight loss had a 76% AF-free rate with or without the use of drugs or ablation, compared with a 59% in the 179 patients with weight fluctuations and the 38% rate in those with no loss or a weight gain. Atrial fibrillation status was assessed by 7-day Holter monitoring at least annually.

Weight fluctuation – defined as a 1% or greater change in weight between two consecutive annual follow-ups – dampened the benefits conferred by weight loss. Patients who experienced more than a 5% weight fluctuation were 2.2-fold more likely to experience AF recurrence than were those without weight fluctuation.

Patients with a sustained 10% weight loss were 5.6-fold more likely to achieve long-term freedom from AF than were patients with lesser or no weight loss. The goal was 10% weight loss rather than a body mass index of 25 kg/m² or less because once AF patients get down to a BMI below 27 kg/m² the incremental benefit of each additional 1 BMI point in terms of freedom from AF becomes much smaller, said Dr. Pathak.

Weight loss also showed a dose-dependent effect on various cardiovascular risk factors. For example, mean systolic blood pressure fell by 18 mm Hg in subjects with at least a 10% weight loss, by 10 mm Hg with a 3%-9% loss, and by 7 mm Hg with a lesser weight loss. Triglycerides, LDL cholesterol, and glycemic control improved in similar fashion. In addition, weight loss showed beneficial effects on cardiac structure, with dose-dependent reductions in left atrial volume indexed for body surface area as well as interventricular septal thickness, Dr. Pathak continued.

He described the weight loss clinic as a “very simple” structured motivational and goal-directed program with face-to-face counseling.

“We have one patient, one physician, no props. We sit with the patient, discuss areas we can improve, then we devise a low-carb, low-fat, high-protein diet in consultation with the patient. Patients maintain a lifestyle journal where they log their meals and exercise. We prescribe at least 200 minutes of moderate-intensity activity per week. Eating is a behavioral pattern, and we have found this approach to be a very effective behavioral tool. Because the plan is developed in consultation with the patient, we’ve found patients tend to adhere to what they have planned,” he explained.

Discussant Dr. Bernard J. Gersh praised LEGACY as “a really wonderful study – very important.

“There are years of epidemiologic evidence suggesting that obesity contributes in a major way to the epidemic of atrial fibrillation, and you’ve taken it a step further,” added Dr. Gersh, professor of medicine at the Mayo Clinic in Rochester, Minn.

Playing devil’s advocate, he asked whether the observed reduction in AF might have nothing to do with weight loss, but could be explained simply by LEGACY perhaps having enrolled a highly compliant group of patients who agreed to attend a clinic and were more willing to take their medications.

Dr. Pathak rejected the compliance factor as an explanation for the results. He noted that while cardiovascular risk factors improved with greater weight loss, the need for antihypertensive, lipid-lowering, and antiarrhythmic medications decreased.

“The effect can’t possibly be due to increased compliance with medications, but rather it’s a true effect of the weight loss itself. So I think this is a true clinical effect and not an epiphenomenon,” he replied.

Dr. Pathak added that he and his coinvestigators are organizing a randomized controlled confirmatory study.

Dr. Prediman K. Shah, who chaired a press conference where the LEGACY study was highlighted, said the study provided him with one of the major take-home lessons from ACC 15.

“We can argue about the mechanism of atrial fibrillation till kingdom come, but the fact is that the association is very strong that weight loss is associated with a reduced burden of atrial fibrillation, and with a very robust magnitude of benefit. That’s one of the messages that I’ll take home with me from this meeting: The next time I see my fat patient with atrial fibrillation, I’m putting him on a weight-reducing diet as the first approach,” declared Dr. Shah, professor of medicine at UCLA and director of the Oppenheimer Atherosclerosis Research Center at Cedars-Sinai Medical Center in Los Angeles.

Dr. Pathak reported having no financial conflicts regarding this study, which was supported by university funds.

Simultaneously with Dr. Pathak’s presentation at ACC 15, the LEGACY study was published online (J. Am. Coll. Cardiol. 2015 [doi: 10.1016/j.jacc.2015.03.002])

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Of the 355 participants, 135 lost at least 10% of their body weight, 103 had a 3%-9% drop in body weight, and 117 had less than a 3% loss or a weight gain.

He described the weight loss clinic as a “very simple” structured motivational and goal-directed program with face-to-face counseling.

Discussant Dr. Bernard J. Gersh praised LEGACY as “a really wonderful study – very important.

Dr. Pathak added that he and his coinvestigators are organizing a randomized controlled confirmatory study.

Dr. Prediman K. Shah, who chaired a press conference where the LEGACY study was highlighted, said the study provided him with one of the major take-home lessons from ACC 15.

Dr. Pathak reported having no financial conflicts regarding this study, which was supported by university funds.

Simultaneously with Dr. Pathak’s presentation at ACC 15, the LEGACY study was published online (J. Am. Coll. Cardiol. 2015 [doi: 10.1016/j.jacc.2015.03.002])

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Of the 355 participants, 135 lost at least 10% of their body weight, 103 had a 3%-9% drop in body weight, and 117 had less than a 3% loss or a weight gain.

He described the weight loss clinic as a “very simple” structured motivational and goal-directed program with face-to-face counseling.

Discussant Dr. Bernard J. Gersh praised LEGACY as “a really wonderful study – very important.

Dr. Pathak added that he and his coinvestigators are organizing a randomized controlled confirmatory study.

Dr. Prediman K. Shah, who chaired a press conference where the LEGACY study was highlighted, said the study provided him with one of the major take-home lessons from ACC 15.

Dr. Pathak reported having no financial conflicts regarding this study, which was supported by university funds.

Simultaneously with Dr. Pathak’s presentation at ACC 15, the LEGACY study was published online (J. Am. Coll. Cardiol. 2015 [doi: 10.1016/j.jacc.2015.03.002])

[email protected]

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Key clinical point: Nearly half of overweight or obese patients with atrial fibrillation who achieved at least a 10% sustained weight loss remained arrhythmia free without resort to antiarrhythmic drugs or ablation procedures during nearly 5 years of follow-up.

Major finding: The weight-loss-associated freedom from recurrent AF was accompanied by improvements in cardiac structure as well as improved cardiovascular risk factors despite lesser use of risk factor-modifying drugs.

Data source: An observational study of 355 overweight or obese patients with atrial fibrillation who agreed to participate in a weight loss clinic.

Disclosures: The LEGACY study was supported by university funds. The presenter reported no financial conflicts.

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