Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease.

PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring quality of care. Adjustment for comorbid illnesses is essential to such studies. Validation of methods to account for comorbid illnesses in administrative data for patients with chronic obstructive pulmonary disease (COPD) has been limited. Our objective was to compare the ability of the Charlson index, the Elixhauser method, and the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict outcomes in patients with COPD.
METHODS: Retrospective cohorts constructed using population-based administrative data of patients with incident (n = 216,735) and prevalent (n = 638,926) COPD in Ontario, Canada, were divided into derivation and validation datasets. The primary outcome was all-cause death within 1 year. Secondary outcomes included all-cause hospitalization, COPD-specific hospitalization, non-COPD hospitalization, and COPD exacerbations.
RESULTS: In both the incident and prevalent COPD cohorts, the three methods had comparable discrimination for predicting mortality (c-statistics in the validation sample of incident patients of 0.819 for the Charlson method versus 0.822 for the Elixhauser method versus 0.830 for the ADG method). All three methods had lower predictive accuracy for predicting nonfatal outcomes.
CONCLUSIONS: In a disease-specific cohort of COPD patients, all three methods allowed for accurate prediction of mortality, with the Johns Hopkins ADGs having marginally higher discrimination.