Faculty Bibliography

STUDY OBJECTIVE: Observation unit admissions have been increasing, a trend that will likely continue because of recent changes in reimbursement policies. The purpose of this study is to determine the effect of the availability of observation units on hospitalizations and discharges to home for emergency department (ED) patients. METHODS: We studied ED visits with a final diagnosis of chest pain in the National Hospital Ambulatory Medical Care Survey from 2007 to 2010. ED visits that resulted in an observation unit admission were propensity-score matched to visits at hospitals without an observation unit. We used logistic regression to develop a prediction model for hospitalization versus discharge home for matched patients treated at nonobservation hospitals. The model was applied to matched observation unit patients to determine the likely alternative disposition had the observation unit not been available. RESULTS: There were 1,325 eligible visits that represented 5,079,154 visits in the United States. Two hundred twenty-seven visits resulted in an observation unit admission. The predictive model for hospitalization had a c statistic of 0.91; variables significantly associated with subsequent hospitalization included age, history of coronary atherosclerosis, systolic blood pressure less than 115 beats/min, and administration of antianginal medications. When the model was applied to matched observation unit patients, 49.9% of them were categorized as discharge home likely. CONCLUSION: In this study, we estimated that half of ED visits for chest pain that resulted in an observation unit admission were made by patients who may have been discharged home had the observation unit not been available. Increased availability of observation units may result in both decreased hospitalizations and decreased discharges to home.

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: Comorbid diabetes is common in heart failure and associated with increased hospitalization and mortality. Nonetheless, the optimal treatment strategy for diabetes in heart failure patients remains poorly characterized, particularly among low income and minority populations. The purpose of this study was to evaluate the association between glycemic control and outcomes among patients with heart failure and diabetes who were seen in a safety net health care system. 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. Subjects with diagnoses of heart failure and diabetes mellitus were included if they had an outpatient visit in 2007-2010 with an HbA1c performed in the prior 90 days. HbA1c and covariates, including demographics, comorbidities, vital signs, labs, and prior utilization, were obtained from the HHC data warehouse, which was linked to the New York State Inpatient Database and to New York State Vital Statistics to ascertain hospitalization and mortality events, respectively. 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 4,723 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%