This study compares the performance of two comorbidity risk adjustment methods (the Deyo et al adaptation of the Charlson index and the Elixhauser et al method) in five groups of California hospital patients with common reasons for hospitalization, and assesses the contribution to model performance made by information drawn from prior hospital admissions.Methods.
California hospital discharge abstract data for the calendar years 1994 through 1997 were used to create a longitudinal data set for patients in the five disease groups. Eleven logistic regression models were estimated to predict the risk of in-hospital death for patients in each group, with both comorbidity risk adjustment methods applied to patient information available from only the index hospitalization, and to information available from both the index and prior hospitalizations.Results.
For every comparison made, the level of statistical performance (area under the receiver operating characteristics curve) demonstrated by models using the Elixhauser et al method was superior to that of models using the Deyo et al adaptation method. Although most patients have information available from prior hospital admissions, this additional information yields only small improvements in the performance of models using either comorbidity risk adjustment method.Conclusions.
Better discrimination is achieved with the Elixhauser et al method using only information from the index hospitalization than is achieved with the Deyo et al adaptation using information from all identified hospital admissions. Both comorbidity risk adjustment methods achieve their best performance when information from the index hospitalization and prior admissions is separated into independent indicators of comorbid illness.