Faculty Bibliography

BACKGROUND: Exercise testing with echocardiography or myocardial perfusion imaging is widely used to risk-stratify patients with suspected coronary artery disease. However, reports of diagnostic performance rarely adjust for referral bias, and this practice may adversely influence patient care. Therefore, we evaluated the potential impact of referral bias on diagnostic effectiveness and clinical decision-making. METHODS AND RESULTS: Searching PubMed and EMBASE (1990-2012), 2 investigators independently evaluated eligibility and abstracted data on study characteristics and referral patterns. Diagnostic performance reported in 4 previously published meta-analyses of exercise echocardiography and myocardial perfusion imaging was adjusted using pooled referral rates and Bayesian methods. Twenty-one studies reported referral patterns in 49 006 patients (mean age 60.7 years, 39.6% women, and 0.8% prior history of myocardial infarction). Catheterization referral rates after normal and abnormal exercise tests were 4.0% (95% CI, 2.9% to 5.0%) and 42.5% (36.2% to 48.9%), respectively, with odds ratio for referral after an abnormal test of 14.6 (10.7 to 19.9). After adjustment for referral, exercise echocardiography sensitivity fell from 84% (80% to 89%) to 34% (27% to 41%), and specificity rose from 77% (69% to 86%) to 99% (99% to 100%). Similarly, exercise myocardial perfusion imaging sensitivity fell from 85% (81% to 88%) to 38% (31% to 44%), and specificity rose from 69% (61% to 78%) to 99% (99% to 100%). Summary receiver operating curve analysis demonstrated only modest changes in overall discriminatory power but adjusting for referral increased positive-predictive value and reduced negative-predictive value. CONCLUSIONS: Exercise echocardiography and myocardial perfusion imaging are considerably less sensitive and more specific for coronary artery disease after adjustment for referral. Given these findings, future work should assess the comparative ability of these and other tests to rule-in versus rule-out coronary artery disease.

BACKGROUND: Hospital care on weekends has been associated with reduced quality and poor clinical outcomes, suggesting that decreases in overall intensity of care may have important clinical effects. We describe a new measure of hospital intensity of care based on utilization of the electronic health record (EHR). METHODS: We measured global intensity of care at our academic medical center by monitoring the use of the EHR in 2011. Our primary measure, termed EHR interactions, was the number of accessions of a patient's electronic record by a clinician, adjusted for hospital census, per unit of time. Our secondary measure was percent of total available central processing unit (CPU) power used to access EHR servers at a given time. RESULTS: EHR interactions were lower on weekend days as compared to weekdays at every hour (P < 0.0001), and the daytime peak in intensity noted each weekday was blunted on weekends. The relative rate and 95% confidence interval (CI) of census-adjusted record accessions per patient on weekdays compared with weekends were: 1.76 (95% CI: 1.74-1.77), 1.52 (95% CI: 1.50-1.55), and 1.14 (95% CI: 1.12-1.17) for day, morning/evening, and night hours, respectively. Percent CPU usage correlated closely with EHR interactions (r = 0.90). CONCLUSIONS: EHR usage is a valid and easily reproducible measure of intensity of care in the hospital. Using this measure we identified large, hour-specific differences between weekend and weekday intensity. EHR interactions may serve as a useful measure for tracking and improving temporal variations in care that are common, and potentially deleterious, in hospital systems. Journal of Hospital Medicine 2013;8:513-518. (c) 2013 Society of Hospital Medicine.

OBJECTIVE To examine the association between diabetes, glycemic control, and risk of fracture-related hospitalization in the Atherosclerosis Risk in Communities (ARIC) Study. RESEARCH DESIGN AND METHODS Fracture-related hospitalization was defined using International Classification of Diseases, 9th revision, codes (733.1-733.19, 733.93-733.98, or 800-829). We calculated the incidence rate of fracture-related hospitalization by age and used Cox proportional hazards models to investigate the association of diabetes with risk of fracture after adjustment for demographic, lifestyle, and behavioral risk factors. RESULTS There were 1,078 incident fracture-related hospitalizations among 15,140 participants during a median of 20 years of follow-up. The overall incidence rate was 4.0 per 1,000 person-years (95% confidence interval [CI], 3.8-4.3). Diagnosed diabetes was significantly and independently associated with an increased risk of fracture (adjusted hazard ratio [HR], 1.74; 95% CI, 1.42-2.14). There also was a significantly increased risk of fracture among persons with diagnosed diabetes who were treated with insulin (HR, 1.87; 95% CI, 1.15-3.05) and among persons with diagnosed diabetes with hemoglobin A1c (HbA1c) >/=8% (1.63; 1.09-2.44) compared with those with HbA1c <8%. Undiagnosed diabetes was not significantly associated with risk of fracture (HR, 1.12; 95% CI, 0.82-1.53). CONCLUSIONS This study supports recommendations from the American Diabetes Association for assessment of fracture risk and implementation of prevention strategies in persons with type 2 diabetes, particularly those persons with poor glucose control.

The purpose of this study was to examine utilization and growth in echocardiography among the general population of Ontario between 2001 and 2009. The age- and sex-adjusted rates of echocardiography grew from 39.1 per 1,000 persons in 2001 to 59.9 per 1,000 persons in 2009, for an annual rate of increase of 5.5%. Repeat echocardiograms increased at a rate of 10.6% per year and accounted for 25.3% of all procedures in 2009 as compared to 18.5% in 2002. While significant increases in echocardiography utilization were observed, opportunities may exist to improve the clinical utility of the echocardiograms performed in Ontario.

OBJECTIVE: We sought to characterize temporal trends in hospitalizations with heart failure as a primary or secondary diagnosis. BACKGROUND: Heart failure patients are frequently admitted for both heart failure and other causes. METHODS: Using the Nationwide Inpatient Sample (NIS), we evaluated trends in heart failure hospitalizations between 2001 and 2009. Hospitalizations were categorized as either primary or secondary heart failure hospitalizations based the location of heart failure in the discharge diagnosis. National estimates were calculated using the sampling weights of the NIS. Age- and gender-standardized hospitalization rates were determined by dividing the number of hospitalizations by the United States population in a given year and using direct standardization. RESULTS: The number of primary heart failure hospitalizations in the United States decreased from 1,137,944 in 2001 to 1,086,685 in 2009, while secondary heart failure hospitalizations increased from 2,753,793 to 3,158,179 over the same period. Age- and gender-adjusted rates of primary heart failure hospitalizations decreased steadily over 2001-2009, from 566 to 468 per 100,000 people. Rates of secondary heart failure hospitalizations initially increased, from 1370 to 1476 per 100,000 from 2001-2006, then decreased to 1359 per 100,000 in 2009. Common primary diagnoses for secondary heart failure hospitalizations included pulmonary disease, renal failure, and infections. CONCLUSIONS: Although primary heart failure hospitalizations declined, rates of hospitalizations with a secondary diagnosis of heart failure were stable in the past decade. Strategies to reduce the high burden of hospitalizations of heart failure patients should include consideration of both cardiac disease and non-cardiac conditions.

BACKGROUND: Heart failure is among the most common reasons for hospitalizations in the United States. Recent data from Medicare suggest that the number of hospitalizations with a primary diagnosis of heart failure has declined over the past decade. However, heart failure may increase hospitalization rates for related comorbidities and individuals with heart failure are commonly admitted for other reasons. Using a nationally representative sample of hospital admissions, we studied trends in hospitalizations with both a primary and a secondary diagnosis of heart failure. METHODS: We evaluated trends in heart failure hospitalizations from 2001 to 2009 using the Nationwide Inpatient Sample (NIS), the largest all-payer inpatient database in the United States. We included hospitalizations with an International Classification of Diseases, Ninth Revision discharge diagnosis codes of 402.X1, 404.X1, 404.X3, 428.XX in any position; these codes in the primary position are used by The Centers for Medicare & Medicaid Services for reporting heart failure quality measures. Admissions were categorized as either primary heart failure hospitalization, if heart failure was the primary discharge code, or heart failure associated hospitalization, if heart failure was listed as a secondary diagnosis. National estimates of heart failure hospitalizations were calculated using the sampling weights and stratified sample design of the NIS. Yearly hospitalization rateswere determined by dividing the number of hospitalizations by the United States population in a given year. Population estimates were obtained from the United States Census Bureau. RESULTS: The total number of heart failure hospitalizations in the United States increased from 3,900,305 in 2001 to 4,398,376 in 2006 and then decreased to 4,253,937 in 2009. The number of primary heart failure admissions decreased from 1,139,607 in 2001 to 1,087,913 in 2009, while the number of heart failure associated hospitalizations increased from 2,760,698 to 3,166,024 over !