Cardiovascular risk prediction model and stratification in patients with type 2 diabetes enrolled in a Medicare Advantage plan

BACKGROUND: Cardiovascular (CV) risk tools have been developed both nationally and internationally to identify patients at risk for developing CV disease or experiencing a CV event. However, these tools vary widely in the definitions of endpoints, the time at which the endpoints are measured, patient populations, and their validity. The primary limitation of some of the most commonly utilized tools is the lack of specificity for a type 2 diabetes (T2D) population and/or among older patients. OBJECTIVE: To develop a predictive model within an older population of patients with T2D to identify patients at risk for CV events. METHODS: This retrospective cohort study used claims, laboratory, and enrollment data during the 2011-2018 study period. Patients with T2D were identified based on diagnoses and/or medications from 2012-2013. The patient cohort was split into 3 different datasets. The holdout dataset included only those patients residing in the northeastern United States. The rest of the sample was then randomly split: 70% for the training dataset, which were used to fit the predictive model, and 30% for the test dataset to assess internal validity. The primary outcome was the first composite CV event defined as at least 1 of the following: inpatient hospitalization for myocardial infarction, ischemic stroke, unstable angina, or heart failure; or any evidence of revascularization. A survival model for the composite outcome was fitted with baseline demographic and clinical characteristics prognostic for the dependent variable utilizing augmented backwards elimination. For assessing model performance, accuracy, sensitivity, specificity, and the c-statistic were used. Patients were ranked as having a low, moderate, or high probability of a future CV event. RESULTS: A total of 362,791 patients were identified. The holdout dataset included only those patients residing in the northeastern United States (n = 8,303). There were 248,142 patients included in the training dataset and 106,346 patients in the test dataset. The proportion with at least 1 observed composite CV event was 20.9%. The final model included 42 variables. The c-statistic was 0.68, and the accuracy, sensitivity, and specificity were approximately 63%. Results were consistent across the training, test, and holdout samples. The optimal cut points minimizing the difference in sensitivity and specificity for low-, moderate-, and high-risk future CV events were determined to be less than 0.18, 0.18-0.63, and greater than 0.63, respectively, in the training dataset at 5 years. The 5-year observed event risk was 11%, 27%, and 51% for patients classified as low, moderate, and high risk of a future CV event, respectively. CONCLUSIONS: A model predicting CV events among older patients with T2D using administrative claims to identify those at risk may be used for focusing interventions to prevent future events.