Quantifying the Risk Continuum for Cardiovascular Death in Adults with Type 2 Diabetes.

OBJECTIVES: In type 2 diabetes (T2D), the most common causes of death are cardiovascular (CV) related, accounting for >50% of deaths in some reports. As novel diabetes therapies reduce CV death risk, identifying patients with T2D at highest CV death risk allows for cost-effective prioritization of these therapies. Accordingly, the primary goal of this study was to quantify the risk continuum for CV death in a real-world T2D population as a means to identify patients with the greatest expected benefit from cardioprotective antidiabetes therapies. METHODS: This retrospective study included patients with T2D receiving services through an integrated health-care system and used data generated through electronic medical records (EMRs). Quantifying the risk continuum entailed developing a prediction model for CV death, creating an integer risk score based on the final prediction model and estimating future CV death risk according to risk score ranking. RESULTS: Among 59,180 patients with T2D followed for an average of 7.5 years, 15,691 deaths occurred, 6,033 (38%) of which were CV related. The EMR-based prediction model included age, established CV disease and risk factors and glycemic indices (c statistic = 0.819). The 10% highest-risk patients according to prediction model elements had an annual CV death risk of ∼5%; the 25% highest-risk patients had an annual risk of ∼2%. CONCLUSIONS: This study incorporated a prediction modelling approach to quantify the risk continuum for CV death in T2D. Prospective application allows us to rank individuals with T2D according to their CV death risk, and may guide prioritization of novel diabetes therapies with cardioprotective properties.

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus.

BACKGROUND: Antidiabetic therapies have shown disparate effects on hospitalization for heart failure (HHF) in clinical trials. This study developed a prediction model for HHF in type 2 diabetes mellitus (T2DM) using real world data to identify patients at high risk for HHF. HYPOTHESIS: Type 2 diabetics at high risk for HHF can be identified using information generated during usual clinical care. METHODS: This electronic medical record- (EMR-) based retrospective cohort study included patients with T2DM free of HF receiving healthcare through a single, large integrated healthcare system. The primary endpoint was HHF, defined as a hospital admission with HF as the primary diagnosis. Cox regression identified the strongest predictors of HHF from 80 candidate predictors derived from EMRs. High risk patients were defined according to the 90th percentile of estimated risk. RESULTS: Among 54,452 T2DM patients followed on average 6.6 years, estimated HHF rates at 1, 3, and 5 years were 0.3%, 1.1%, and 2.0%. The final 9-variable model included: age, coronary artery disease, blood urea nitrogen, atrial fibrillation, hemoglobin A1c, blood albumin, systolic blood pressure, chronic kidney disease, and smoking history (c = 0.782). High risk patients identified by the model had a >5% probability of HHF within 5 years. CONCLUSIONS: The proposed model for HHF among T2DM demonstrated strong predictive capacity and may help guide therapeutic decisions.