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Development of algorithms for identifying fatal cardiovascular disease in Medicare claims.

ABSTRACT: BACKGROUND:Cause of death is often not available in administrative claims data. OBJECTIVE:To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; ie, fatal coronary heart disease [CHD] or stroke), CHD, and stroke. METHODS:Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standard. Stepwise selection was used to choose predictors from Medicare data for inclusion in the algorithms. C-index, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to assess algorithm performance. Net reclassification index (NRI) was used to compare the algorithms with an approach of classifying all deaths within 28 days following hospitalization for myocardial infarction and stroke to be fatal CVD. RESULTS:Data from 2,685 REGARDS participants with linkage to Medicare, who died between 2003 and 2013, were analyzed. The C-index for discriminating fatal CVD from other causes of death was 0.87. Using a cut-point that provided the closest observed-to-predicted number of fatal CVD events, the sensitivity was 0.64, specificity 0.90, PPV 0.65, and NPV 0.90. The algorithms resulted in positive NRIs compared with using deaths within 28 days following hospitalization for myocardial infarction and stroke. Claims-based algorithms for discriminating fatal CHD and fatal stroke performed similarly to fatal CVD. CONCLUSION:The claims-based algorithms developed to discriminate fatal CVD events from other causes of death performed better than the method of using hospital discharge diagnosis codes.

SUBMITTER: Xie F

PROVIDER: S-EPMC7050209 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Development of algorithms for identifying fatal cardiovascular disease in Medicare claims.

Xie Fenglong F Colantonio Lisandro D LD Curtis Jeffrey R JR Kilgore Meredith L ML Levitan Emily B EB Monda Keri L KL Safford Monika M MM Taylor Ben B Woodward Mark M Muntner Paul P

Pharmacoepidemiology and drug safety 20180314 7

<h4>Background</h4>Cause of death is often not available in administrative claims data.<h4>Objective</h4>To develop claims-based algorithms to identify deaths due to fatal cardiovascular disease (CVD; ie, fatal coronary heart disease [CHD] or stroke), CHD, and stroke.<h4>Methods</h4>Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data were linked with Medicare claims to develop the algorithms. Events adjudicated by REGARDS study investigators were used as the gold standar ...[more]

PMID: 29537120

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Project description: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.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.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%).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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Development of algorithms for identifying fatal cardiovascular disease in Medicare claims.

Publications

Development of algorithms for identifying fatal cardiovascular disease in Medicare claims.

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