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Member Research and Reports

Member Research and Reports

UAB Develops Algorithms for Identifying Medicare Beneficiaries at Risk for CHD

Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. However, claims data do not include all of the factors used for risk stratification in clinical care. Therefore, Dr. Emily B. Levitan, assistant professor in the department of epidemiology at the University of Alabama at Birmingham, sought to develop claims-based algorithms to identify individuals at high estimated risk for coronary heart disease (CHD) events and to identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.

Co-investigators were Dr. Paul Muntner, professor; Dr. Elizabeth Delzell, professor; and Mr. Hong Zhao, statistician I, also in the department of epidemiology; Dr. Monika M. Safford, professor in the division of preventive medicine; Dr. Jeffrey R. Curtis, associate professor in the division of clinical immunology and rheumatology; Dr. Vera A. Bittner, professor, and Dr. Todd M. Brown, assistant professor, in the division of cardiovascular disease; and former department colleague Dr. Evan L. Thacker (currently at Brigham Young University).

The team conducted a cross-sectional analysis of 6,615 participants at or below 66 years old, using data from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study baseline visit in 2003–2007 linked to Medicare claims data. Using this REGARDS data, they defined high risk for CHD events as having a history of CHD, at least one risk equivalent, or a Framingham CHD risk score under 20 percent. Among statin users at high risk for CHD events, Dr. Levitan and her fellow researchers defined uncontrolled LDL cholesterol as LDL cholesterol equal to or below 100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and health care utilization, they developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.

REGARDS data indicated that 49 percent of participants were at high risk for CHD events. A claims-based algorithm identified high risk for CHD events with a positive predictive value of 87 percent, sensitivity of 69 percent, and specificity of 90 percent. Among statin users at high risk for CHD events, 30 percent had LDL cholesterol equal to or below 100 mg/dL. A claims-based algorithm identified LDL cholesterol equal to or below 100 mg/dL with a positive predictive value of 43 percent, sensitivity of 19 percent, and specificity of 89 percent. Although sensitivity was low, the team concluded that the high positive predictive value of their algorithm supports the use of claims to identify Medicare beneficiaries at high risk for CHD events. “Claims-Based Algorithms for Identifying Medicare Beneficiaries at High Estimated Risk for Coronary Heart Disease Events: A Cross-Sectional Study” was published in April in BMC Health Services Research.

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