Recalibration and validation of the Charlson Comorbidity Index in an Asian population: the National Health Insurance Service-National Sample Cohort study

Abstract

Abstract Weights assigned to comorbidities in predicting mortality may vary based on the type of index disease and advances in the management of comorbidities. We aimed to develop a modified version of the Charlson Comorbidity Index (CCI) using an Asian nationwide database (mCCI-A), enabling the precise prediction of mortality rates in this population. The main data source used in this study was the National Health Insurance Service-National Sample Cohort (NHIS-NSC) obtained from the National Health Insurance database, which includes health insurance claims filed between January 1, 2002, and December 31, 2013, in Korea. Of the 1,025,340 individuals included in the NHIS-NSC, 570,716 patients who were hospitalized at least once were analyzed in this study. In total, 399,502 patients, accounting for 70% of the cohort, were assigned to the development cohort, and the remaining patients (n = 171,214) were assigned to the validation cohort. The mCCI-A scores were calculated by summing the weights assigned to individual comorbidities according to their relative prognostic significance determined by a multivariate Cox proportional hazard model. The modified index was validated in the same cohort. The Cox proportional hazard model provided reassigned severity weights for 17 comorbidities that significantly predicted mortality. Both the CCI and mCCI-A were correlated with mortality. However, compared with the CCI, the mCCI-A showed modest but significant increases in the c statistics. According to the analyses using continuous net reclassification improvement, the mCCI-A improved the net mortality risk reclassification by 44.0% (95% confidence intervals (CI), 41.6–46.5; p < 0.001). The mCCI-A facilitates better risk stratification of mortality rates in Korean inpatients than the CCI, suggesting that the mCCI-A may be a preferable index for use in clinical practice and statistical analyses in epidemiological studies.