A Revised Comorbidity Model for Administrative Databases Using Clinical Classifications Software Refined Variables

Background and objective Database research has shaped policies, identified trends, and informed healthcare guidelines for numerous disease conditions. However, despite their abundant uses and vast potential, administrative databases have several limitations. Adjusting outcomes for comorbidities is often needed during database analysis as a means of overcoming non-randomization. We sought to obtain a model for comorbidity adjustment based on Clinical Classifications Software Refined (CCSR) variables and compare this with current models. Our aim was to provide a simplified, adaptable, and accurate measure for comorbidities in the Agency for Healthcare Research and Quality (AHRQ) databases, in order to strengthen the validity of outcomes. Methods The Nationwide Inpatient Sample (NIS) database for 2018 was the data source. We obtained the mortality rate among all included hospitalizations in the dataset. A model based on CCSR categories was mapped from disease groups in Sundararajan's adaptation of the modified Deyo’s Charlson Comorbidity Index (CCI). We employed logistic regression analysis to obtain the final model using CCSR variables as binary variables. We tested the final model on the 10 most common reasons for hospitalizations. Results The model had a higher area under the curve (AUC) compared to the three modalities of the CCI studied in all the categories. Also, the model had a higher AUC compared to the Elixhauser model in 8/10 categories. However, the model did not have a higher AUC compared to a model made from stepwise backward regression analysis of the original 21-variable model. Conclusion We developed a 15-CCSR-variable model that showed good discrimination for inpatient mortality compared to prior models.