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

Abstract

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.

Keywords: diabetes; heart failure; risk prediction.

Figure 1

Cumulative incidence rates for heart failure hospitalization: overall and by history of heart failure at the index date

Figure 2

Cumulative incidence rates for heart failure hospitalization by risk score strata