Abstract
BackgroundDatabases 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.MethodsWe 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.ResultsREGARDS 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%).ConclusionsAlthough 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.
Highlights
Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications
We describe claims-based algorithms to identify uncontrolled low-density lipoprotein (LDL) cholesterol according to Adult Treatment Panel III (ATP III) guidelines among statin users at high risk for coronary heart disease (CHD) events
In the model that included pre-specified variables, a predicted probability threshold of 0.55 yielded a positive predictive value (PPV) of 87% for identifying high risk for CHD, and a sensitivity of 69%; results were similar after adding data mining variables (Table 2 and see Additional file 1: Figure S1, Panel A)
Summary
Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. 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. Novel LDL cholesterol lowering medications are being evaluated in clinical trials [6,7] If these medications obtain regulatory approval, healthcare claims data could be used for comparative effectiveness and pharmacovigilance studies [8]. One potential barrier is that claims data do not include clinical or laboratory values that are often used to estimate CHD event risk. Some data show that claims-based algorithms can be useful in identifying high risk groups, for example people at high risk for osteoporotic fracture [9]. Whether claimsbased algorithms can be used to identify individuals at high risk for CHD events is not known
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