Comorbidity indices to predict mortality from Medicare data: results from the national registry of atrial fibrillation.

Background: By accounting for level of comorbidity, risk-adjustment models should quantify the risk of death. How accurately comorbidity indices predict risk of death in Medicare beneficiaries with atrial fibrillation is unclear.

Objectives: We sought to quantify how well 3 administrative-data based comorbidity indices (Deyo, Romano, and Elixhauser) predict mortality compared with a chart-review index.

Design: We undertook a retrospective cohort study using Medicare claim data (1995-1999) and medical record review.

Subjects: We studied Medicare beneficiaries (n = 2728; mean age = 77) with a common cardiac dysrhythmia, atrial fibrillation.

Measures: The outcome was time to death with the accuracy of the comorbidity indices measured by the c-statistic.

Results: Correlation between Deyo and Romano indices was strong, but weak between them and the other indices. Prevalence of many comorbidity conditions varied with different indices. Compared with demographic data alone (c = 0.64), all comorbidity indices predicted death significantly (P < 0.001) better: the c index was 0.76 for Deyo, 0.78 for Romano, 0.76 for Elixhauser, and 0.75 for medical record review. The 95% confidence intervals of the c-statistic for the 4 indices overlapped with one another. Key comorbidity conditions for death included metastatic cancer, neuropsychiatric disease, heart failure, and liver disease.

Conclusion: The predictive accuracy of 3 administrative-data based indices was similar and comparable with chart-review.