A cardiovascular disease risk prediction algorithm for use with the Medicare current beneficiary survey
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Published on: January 8, 2020
Hassan Fouayzi1, Arlene S Ash2, Amy K Rosen3,4
1Meyers Primary Care Institute (A Joint Endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health), Worcester, Massachusetts.
Insights
A new cardiovascular disease (CVD) risk score was developed using Medicare Current Beneficiary Survey (MCBS) data. This score quantifies CVD risk in Medicare beneficiaries, aiding health policy and research.
Area of Science:
- Gerontology
- Epidemiology
- Health Services Research
Background:
- Cardiovascular disease (CVD) poses a significant health burden on the Medicare population.
- Existing CVD risk scores may not fully utilize data available within the Medicare Current Beneficiary Survey (MCBS).
Purpose of the Study:
- To develop and validate a novel CVD risk score specifically for use with MCBS data.
- To enhance the quantification of CVD risk within the Medicare beneficiary population.
Main Methods:
- Utilized backward stepwise selection and Cox proportional hazards regressions on MCBS data (1999-2013).
- Included traditional Framingham Risk Score (FRS) predictors plus morbidity burden, general health status, and functional limitation.
- Validated the model using C statistics and calibration plots.
Main Results:
- The final MCBS-based CVD risk score demonstrated fair discrimination (C-statistic=0.69) and good calibration.
- The model performed well upon validation (C-statistic=0.68).
- Approximately 5% of participants developed a CVD event during the follow-up period.
Conclusions:
- A new CVD risk score is available for use with MCBS data.
- This score extends the utility of MCBS for assessing CVD risk in Medicare beneficiaries.
- Informs health policy and health services research for the Medicare population.
Objective:
To 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 Design:
We 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 Methods:
We 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 Findings:
About 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.
Conclusions:
Our 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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