Abstract 11917: Cardiometabolic Disease Staging and Major Adverse Cardiovascular Event Prediction in Two Prospective Cohorts

Abstract

Background: Cardiometabolic risk prediction models that incorporate the presence and severity of metabolic syndrome traits to predict cardiovascular outcomes may help identify high-risk populations early in the progression of cardiometabolic disease. Objective: To examine whether a modified Cardiometabolic Disease Staging System (CMDS) - a validated diabetes prediction model - predicts major adverse cardiovascular events (MACE) in two prospective cohorts. Methods: We developed a predictive model using data accessible in clinical practice [fasting glucose, blood pressure, body mass index, cholesterol, triglycerides, smoking status, diabetes status, hypertension medication use] from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study using Bayesian logistic regression - with external validation in the Atherosclerosis Risk in Communities (ARIC) study - to predict MACE [cardiovascular death, nonfatal myocardial infarction, and/or nonfatal stroke]. Predictive performance was assessed using receiver operating characteristic (ROC) curves, mean squared errors, misclassification and area under the curve (AUC) statistics. Results: This analysis included 20,234 REGARDS participants who were enrolled at baseline (2003-2007) and had no history of stroke or myocardial infarction. Participants were a mean age of 64 years (SD 9.3), mostly female (58%), 41% were non-Hispanic Black, and 18% had diabetes. During a median 10-year follow-up, there were 2,695 incident MACE (13.3%). The CMDS development model in REGARDS for MACE had an AUC of 0.721; with an AUC of 0.737 in external validation (ARIC). Our CMDS model performed similarly to both the ACC/AHA 10-year risk estimate (AUC 0.721 vs 0.716) and the Framingham risk score (AUC 0.673). Conclusions: We found that the CMDS predicted the onset of major cardiovascular events with good predictive ability and performed similarly or better than two commonly known CVD prediction risk tools. These data underscore the importance of insulin resistance as a cardiovascular disease risk factor, and that CMDS can be used to identify individuals at high-risk for progression to CVD in order to enhance the efficacy and benefit/risk ratio for interventions for CMD prevention.