ArticleHealth services research2020
A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.
Article in Health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Adherence to GLP-1 receptor agonists and SGLT2 inhibitors by out-of-pocket spending among Medicare beneficiaries with diabetes.Diabetes, obesity & metabolism · 2025Article
- Diagnostic Policies Optimization for Chronic Diseases Based on POMDP Model.Healthcare (Basel, Switzerland) · 2022Article
- A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey.Health services research · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveTo develop a cardiovascular disease (CVD) risk score that can be used to quantify CVD risk in the Medicare Current Beneficiary Survey (MCBS). DATA SOURCES: We used 1999-2013 MCBS data. STUDY
designWe used a backward stepwise approach and cox proportional hazards regressions to build and validate a new CVD risk score, similar to the Framingham Risk Score (FRS), using only information available in MCBS. To assess its performance, we calculated C statistics and examined calibration plots. DATA COLLECTION/EXTRACTION
methodsWe studied 21 968 community-dwelling Medicare beneficiaries aged 65 years or older without pre-existing CVD. We obtained risk factors from both survey and claims data. We used claims data to derive "CVD event within 3 years" following the FRS definition of CVD. PRINCIPAL
findingsAbout five percent of MCBS participants developed a CVD event over a mean follow-up period of 348 days. Our final MCBS-based model added morbidity burden, reported general health status, and functional limitation to the traditional FRS predictors of CVD. This model had relatively fair discrimination (C statistic = 0.69; 95% confidence interval [CI], 0.67-0.71) and performed well on validation (C = 0.68; CI, 0.66-0.70). More importantly, the plot of observed CVD outcomes versus predicted ones showed that this model had a good calibration.
conclusionsOur new CVD risk score can be calculated using MCBS data, thereby extending the survey's ability to quantify CVD risk in the Medicare population and better inform both health policy and health services research.
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