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Claims-based algorithms for identifying Medicare beneficiaries at high estimated risk for coronary heart disease
Evan L Thacker, Paul Muntner, Hong Zhao
1Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA. elevitan@uab.edu.
Insights
Claims data can identify high coronary heart disease (CHD) risk in older adults with 87% accuracy. Algorithms using Medicare claims effectively identify individuals needing cardiovascular risk management.
Area of Science:
- Cardiovascular research
- Health services research
- Epidemiology
Background:
- Medical claims databases are valuable for cardiovascular research, including comparative effectiveness and pharmacovigilance.
- Claims data lack comprehensive risk stratification factors used in clinical care.
- Developing claims-based algorithms is crucial for identifying high-risk individuals and managing conditions like uncontrolled LDL cholesterol.
Purpose of the Study:
- To develop claims-based algorithms for identifying individuals at high risk for coronary heart disease (CHD) events.
- To identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.
Main Methods:
- Cross-sectional analysis of 6,615 participants (≥66 years) from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study linked to Medicare claims.
- Defined high CHD risk using history of CHD, risk equivalents, or Framingham CHD risk score >20%.
- Defined uncontrolled LDL cholesterol (≥100 mg/dL) among statin users at high CHD risk.
Main Results:
- 49% of participants were identified as high risk for CHD events.
- The claims-based algorithm for high CHD risk achieved 87% positive predictive value, 69% sensitivity, and 90% specificity.
- Among high-risk statin users, 30% had uncontrolled LDL cholesterol (≥100 mg/dL).
- The claims-based algorithm for uncontrolled LDL cholesterol had 43% positive predictive value, 19% sensitivity, and 89% specificity.
Conclusions:
- The high positive predictive value of the algorithm for high CHD risk supports its use in identifying Medicare beneficiaries.
- Despite low sensitivity, the algorithm's accuracy in identifying high-risk individuals is valuable for cardiovascular research using claims data.
- Further refinement of algorithms may improve sensitivity for identifying uncontrolled LDL cholesterol in high-risk populations.
Background:
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. We 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.
Methods:
We conducted a cross-sectional analysis of 6,615 participants ≥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 REGARDS data we defined high risk for CHD events as having a history of CHD, at least 1 risk equivalent, or Framingham CHD risk score >20%. Among statin users at high risk for CHD events we defined uncontrolled LDL cholesterol as LDL cholesterol ≥100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and healthcare utilization, we developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.
Results:
REGARDS data indicated that 49% 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% (95% CI: 85%, 88%), sensitivity of 69% (95% CI: 67%, 70%), and specificity of 90% (95% CI: 89%, 91%). Among statin users at high risk for CHD events, 30% had LDL cholesterol ≥100 mg/dL. A claims-based algorithm identified LDL cholesterol ≥100 mg/dL with a positive predictive value of 43% (95% CI: 38%, 49%), sensitivity of 19% (95% CI: 15%, 22%), and specificity of 89% (95% CI: 86%, 90%).
Conclusions:
Although the sensitivity was low, the high positive predictive value of our algorithm for high risk for CHD events supports the use of claims to identify Medicare beneficiaries at high risk for CHD events.
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