Objective: The Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) are widely used to study comorbid conditions but these indices have not been validated in Japanese datasets. In this study, our objective was to validate and recalibrate CCI and ECI in a Japanese insurance claims database.
Methods: All hospitalizations for patients aged≥18 years discharged between January 2011 and December 2016 were randomly allocated to derivation and validation cohorts. Predictability for hospital death and re-admission was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions at admission month or the derived score in the derivation cohort. After stepwise variable selection, weighted risk scores for each condition were re-assigned using odds ratios (CCI) or beta coefficients (ECI). The modified models were evaluated in the validation cohort.
Results: The original CCI/ECI had good discriminatory power for hospital death: C statistics (95％ confidence interval) for individual comorbidities and score models were 0.845 (0.835-0.855) and 0.823 (0.813-0.834) for CCI, and 0.839 (0.828-0.850) and 0.801 (0.790-0.812) for ECI, respectively. Modified CCI and ECI had reduced numbers of comorbidities (17 to 10 and 30 to 21, respectively) but maintained comparable discriminatory abilities: C statistics for modified individual comorbidities and score models were 0.843 (0.833-0.854) and 0.838 (0.827-0.848) for CCI, and 0.840 (0.828-0.852) and 0.839 (0.827-0.851) for ECI, respectively.
Conclusions: The original and modified models showed comparable discriminatory abilities and both can be used in future studies using insurance claims databases.