Faculty Bibliography

BACKGROUND: Comorbid diabetes is common in heart failure and associated with increased hospitalization and mortality. Nonetheless, the association between glycemic control and outcomes among patients with heart failure and diabetes remains poorly characterized, particularly among low income and minority patients. METHODS: We performed a retrospective cohort study of outpatients with heart failure and diabetes in the New York City Health and Hospitals Corporation, the largest municipal health care system in the United States. Cox proportional hazard models were used to measure the association between HbA1c levels and outcomes of all-cause hospitalization, heart failure hospitalization, and mortality. RESULTS: Of 4723 patients with heart failure and diabetes, 42.6 % were black, 30.5 % were Hispanic/Latino, 31.4 % were Medicaid beneficiaries and 22.9 % were uninsured. As compared to patients with an HbA1c of 8.0-8.9 %, patients with an HbA1c of <6.5, 6.5-6.9, 7.0-7.9, and >/=9.0 % had an adjusted hazard ratio (aHR) (95 % CI) for all-cause hospitalization of 1.03 (0.90-1.17), 1.05 (0.91-1.22), 1.03 (0.90-1.17), and 1.13 (1.00-1.28), respectively. An HbA1c >/= 9.0 % was also associated with an increased risk of heart failure hospitalization (aHR 1.33; 95 % CI 1.11-1.59) and a non-significant increased risk in mortality (aHR 1.20; 95 % CI 0.99-1.45) when compared to HbA1c of 8.0-8.9 %. CONCLUSIONS: Among a cohort of primarily minority and low income patients with heart failure and diabetes, an increased risk of hospitalization was observed only for an HbA1c greater than 9 %.

BACKGROUND: Appropriate use criteria (AUC) for cardiac stress tests address concerns about utilization growth and patient safety. We systematically reviewed studies of appropriateness, including within physician specialties; evaluated trends over time and in response to AUC updates; and characterized leading indications for inappropriate/rarely appropriate testing. METHODS: We searched PubMed (2005-2015) for English-language articles reporting stress echocardiography or myocardial perfusion imaging (MPI) appropriateness. Data were pooled using random-effects meta-analysis and meta-regression. RESULTS: Thirty-four publications of 41,578 patients were included, primarily from academic centers. Stress echocardiography appropriate testing rates were 53.0% (95% CI, 45.3%-60.7%) and 50.9% (42.6%-59.2%) and inappropriate/rarely appropriate rates were 19.1% (11.4%-26.8%) and 28.4% (23.9%-32.8%) using 2008 and 2011 AUC, respectively. Stress MPI appropriate testing rates were 71.1% (64.5%-77.7%) and 72.0% (67.6%-76.3%) and inappropriate/rarely appropriate rates were 10.7% (7.2%-14.2%) and 15.7% (12.4%-19.1%) using 2005 and 2009 AUC, respectively. There was no significant temporal trend toward rising rates of appropriateness for stress echocardiography or MPI. Unclassified stress echocardiograms fell by 79% (p = 0.04) with updated AUC. There were no differences between cardiac specialists and internists. CONCLUSIONS: Rates of appropriate use tend to be lower for stress echocardiography compared to MPI, and updated AUC reduced unclassified stress echocardiograms. There is no conclusive evidence that AUC improved appropriate use over time. Further research is needed to determine if integration of appropriateness guidelines in academic and community settings is an effective approach to optimizing inappropriate/rarely appropriate use of stress testing and its associated costs and patient harms.

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

BACKGROUND: Hospital care on weekends has been associated with delays in care, reduced quality, and poor clinical outcomes. OBJECTIVE: The purpose of this study was to evaluate the impact of a weekend hospital intervention on processes of care and clinical outcomes. The multifaceted intervention included expanded weekend diagnostic services, improved weekend discharge processes, and increased physician and care management services on weekends. DESIGN AND PATIENTS: This was an interrupted time series observational study of adult non-obstetric patients hospitalized at a single academic medical center between January 2011 and January 2014. The study included 18 months prior to and 19 months following the implementation of the intervention. Data were analyzed using segmented regression analysis with adjustment for confounders. MAIN MEASURES: The primary outcome was average length of stay. Secondary outcomes included percent of patients discharged on weekends, 30-day readmission rate, and in-hospital mortality rate. KEY RESULTS: The study included 57,163 hospitalizations. Following implementation of the intervention, average length of stay decreased by 13 % (95 % CI 10-15 %) and continued to decrease by 1 % (95 % CI 1-2 %) per month as compared to the underlying time trend. The proportion of weekend discharges increased by 12 % (95 % CI 2-22 %) at the time of the intervention and continued to increase by 2 % (95 % CI 1-3 %) per month thereafter. The intervention had no impact on readmissions or mortality. During the post-implementation period, the hospital was evacuated and closed for 2 months due to damage from Hurricane Sandy, and a new hospital-wide electronic health record was introduced. The contributions of these events to our findings are not known. We observed a lower inpatient census and found differences in patient characteristics, including higher rates of Medicaid insurance and comorbidities, in the post-Hurricane Sandy period as compared to the pre-Sandy period. CONCLUSIONS: The intervention was associated with a reduction in length of stay and an increase in weekend discharges. Our longitudinal study also illuminated the challenges of evaluating the effectiveness of a large-scale intervention in a real-world hospital setting.

BACKGROUND: Randomized trials of percutaneous coronary intervention (PCI) versus coronary artery bypass graft (CABG) routinely exclude patients with chronic kidney disease (CKD). OBJECTIVES: This study evaluated outcomes of PCI versus CABG in patients with CKD. METHODS: Patients with CKD who underwent PCI using everolimus-eluting stents were propensity-score matched to patients who underwent isolated CABG for multivessel coronary disease in New York. The primary outcome was all-cause mortality. Secondary outcomes were myocardial infarction (MI), stroke, and repeat revascularization. RESULTS: Of 11,305 patients with CKD, 5,920 patients were propensity-score matched. In the short term, PCI was associated with a lower risk of death (hazard ratio [HR]: 0.55; 95% confidence interval [CI]: 0.35 to 0.87), stroke (HR: 0.22; 95% CI: 0.12 to 0.42), and repeat revascularization (HR: 0.48; 95% CI: 0.23 to 0.98) compared with CABG. In the longer term, PCI was associated with a similar risk of death (HR: 1.07; 95% CI: 0.92 to 1.24), higher risk of MI (HR: 1.76; 95% CI: 1.40 to 2.23), a lower risk of stroke (HR: 0.56; 95% CI: 0.41 to 0.76), and a higher risk of repeat revascularization (HR: 2.42; 95% CI: 2.05 to 2.85). In the subgroup with complete revascularization with PCI, the increased risk of MI was no longer statistically significant (HR: 1.18; 95% CI: 0.67 to 2.09). In the 243 matched pairs of patients with end-stage renal disease on hemodialysis, PCI was associated with significantly higher risk of death (HR: 2.02; 95% CI: 1.40 to 2.93) and repeat revascularization (HR: 2.44; 95% CI: 1.50 to 3.96) compared with CABG. CONCLUSIONS: In patients with CKD, CABG is associated with higher short-term risk of death, stroke, and repeat revascularization, whereas PCI with everolimus-eluting stents is associated with a higher long-term risk of repeat revascularization and perhaps MI, with no long-term mortality difference. In the subgroup on dialysis, the results favored CABG over PCI.