Response to Comment on Segar et al. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score. Diabetes Care 2019;42:2298–2306

Identifying patients with type 2 diabetes mellitus (T2DM) at high risk for future heart failure (HF) has been challenging given the multisystem inputs that contribute to HF risk, inaccuracies in administrative coded data, and complexities with risk prediction models. In the machine learning–derived WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) score, we considered 147 candidate variables to create a simple, user-friendly, integer-based risk score to predict adjudicated incident HF events (1). We appreciate the critical appraisal of WATCH-DM by Fonseca and colleagues (2). Our integer score was developed similarly to the well-established method popularized by the Framingham framework, in which the points associated with each level of each risk factor are relative to the points associated with an increase in age (3). Briefly, continuous variables were first converted to dichotomous variables. Cutoffs for the continuous variables were either determined by established guidelines (for example, the normal, overweight, and obese cutoffs for …