Abstract 17348: Cardiovascular Risk Prediction in Diabetes From Machine Learning: The ACCORD Study

Abstract

Background: Atherosclerotic cardiovascular disease (ASCVD) risk prediction in persons with type 2 diabetes (T2DM) using existing calculators is imprecise. We aimed to develop a machine-learning (ML) model for prediction of ASCVD events in adults with T2DM. Methods: We utilized subjects with T2DM from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial without known CVD and calculated their 10-year ASCVD risk using the ACC/AHA pooled cohort risk calculator (PCRC) predicting the composite outcome of myocardial infarction, non-fatal stroke and cardiovascular death using age, gender, race, systolic blood pressure (SBP), antihypertensive medication use, total cholesterol, high-density lipoprotein cholesterol, current smoking status and diabetes mellitus status. We developed an ASCVD risk calculator based on Random Forest (RF) ML algorithms using follow-up data from ACCORD with the same 9 predictors. 5-fold stratified random split was applied as cross-validation strategies. Results: A total of 6581 T2DM participants without baseline ASCVD were included in our final sample with a median follow up of 9.1 years. The performance of PCRC was modest with an AUC=0.604. In contrast, the ML model had much better performance with a RF AUC=0.866. The figure shows the rank of feature importance (%) from random forest modeling (from high to low): age, systolic blood pressure, total cholesterol, HDL-C, female gender, White ethnicity, current smoker, hypertension treatment. Conclusion: The ML ASCVD Risk Calculator outperforms the AHA/ACC PCRC in predicting ASCVD outcomes among those with T2DM from the ACCORD trial. Age, SBP, total cholesterol and HDL-C were the most important features in ASCVD prediction among those with T2DM. Future studies need to validate these and other ML algorithms and to explore their applicability in guidelines.