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. DISCLOSURES: This study was funded by Boehringer Ingelheim (BI) and conducted as part of the BI-Humana Research Collaboration. Caplan is employed by Humana Healthcare Research, Inc., a wholly owned subsidiary of Humana Inc., which received fees to conduct the study from the sponsor BI. At the time of the study, Hayden and Harvey were employees of Humana Healthcare Research, Inc. Additionally, Prewitt, who owns stock in Humana Inc, and Chiguluri are employees of Humana Inc. Kattan, associated with the Cleveland Clinic in Ohio, served as a consultant to BI, and Pimple and Goss are employees of BI. Luthra was employed by BI for the duration of the study. Portions of this work were accepted as an abstract and presented as a poster at the American Diabetes Association 2020 virtual meeting, June 12-16, 2020.

Health Care Utilization and Medical Cost Outcomes from a Digital Diabetes Prevention Program in a Medicare Advantage Population.

This study examined the effects of a digital diabetes prevention program (DPP) on health care costs and utilization among Medicare Advantage participants. Patients (n = 501) received access to a plan-sponsored, digitally-delivered DPP accessible through computer, tablet, or smartphone. Prior research demonstrated a 7.5% reduction in body weight at 12 months. A comparison group who did not participate in the DPP was constructed by matching on demographic, health plan, health status, and health care costs and utilization. The authors assessed effects on cost and utilization outcomes using difference-in-differences regressions, controlling for propensities to participate and engage in the DPP, in the 12 months prior to DPP enrollment and 24 months after. Though post-enrollment data showed trends in decreased drug spending and emergency department use, increased inpatient utilization, and no change in total nondrug costs or outpatient utilization, the findings did not reach statistical significance, potentially because of sample size. The population had low costs and utilization at baseline, which may be responsible for the lack of observed effects in the short time frame. This study demonstrates the challenges of studying the effectiveness of preventive programs in a population with low baseline costs and the importance of using a large enough sample and follow-up period, but remains an important contribution to exploring the effects of digital DPPs in a real-world sample of individuals who were eligible and willing to participate.

Outcomes of a Digital Health Program With Human Coaching for Diabetes Risk Reduction in a Medicare Population.

OBJECTIVE: To examine the outcomes of a Medicare population who participated in a program combining digital health with human coaching for diabetes risk reduction. METHOD: People at risk for diabetes enrolled in a program combining digital health with human coaching. Participation and health outcomes were examined at 16 weeks and 6 and 12 months. RESULTS: A total of 501 participants enrolled; 92% completed at least nine of 16 core lessons. Participants averaged 19 of 31 possible opportunities for weekly program engagement. At 12 months, participants lost 7.5% ( SD = 7.8%) of initial body weight; among participants with clinical data, glucose control improved (glycosylated hemoglobin [HbA1c] change = -0.14%, p = .001) and total cholesterol decreased (-7.08 mg/dL, p = .008). Self-reported well-being, depression, and self-care improved ( p < .0001). DISCUSSION: This Medicare population demonstrated sustained program engagement and improved weight, health, and well-being. The findings support digital programs with human coaching for reducing chronic disease risk among older adults.