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.
BT - Pharmacoepidemiol Drug Saf C1 - https://www.ncbi.nlm.nih.gov/pubmed/29537120?dopt=Abstract DO - 10.1002/pds.4421 J2 - Pharmacoepidemiol Drug Saf LA - eng N2 -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.
PY - 2018 T2 - Pharmacoepidemiol Drug Saf TI - Development of algorithms for identifying fatal cardiovascular disease in Medicare claims. SN - 1099-1557 ER -