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.


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


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


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


METHODS
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.


CONCLUSIONS
The predictive accuracy of 3 administrative-data based indices was similar and comparable with chart-review.
Yan
Yan
Y
Division of General Medical Sciences, Washington University School of Medicine, St. Louis, MO 63110, USA.
Birman-Deych
Elena
E
Radford
Martha J
MJ
Nilasena
David S
DS
Gage
Brian F
BF
D000368:Aged; D001281:Atrial Fibrillation / Q000401:mortality*; D015331:Cohort Studies; D015897:Comorbidity; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D006278:Medicare; D016016:Proportional Hazards Models; D012042:Registries; D012189:Retrospective Studies; D012720:Severity of Illness Index; D014481:United States / Q000453:epidemiology