Development of a Surgery-Specific Comorbidity Score for Use in Administrative Data.

Abstract

To create a novel comorbidity score tailored for surgical database research. Despite their use in surgical research, the Elixhauser (ECI) and Charlson Comorbidity Indices (CCI) were developed nearly four decades ago utilizing primarily non-surgical cohorts. Adults undergoing 62 operations across 14 specialties were queried from the 2019 National Inpatient Sample (NIS) using International Classification of Diseases, 10th Revision (ICD-10) codes. ICD-10 codes for chronic diseases were sorted into Clinical Classifications Software Refined (CCSR) groups. CCSR with non-zero feature importance across four machine learning algorithms predicting in-hospital mortality were used for logistic regression; resultant coefficients were used to calculate the Comorbid Operative Risk Evaluation (CORE) score based on previously validated methodology. Areas under the receiver operating characteristic (AUROC) with 95% Confidence Intervals (CI) were used to compare model performance in predicting in-hospital mortality for the CORE score, ECI, and CCI. Validation was performed using the 2016-2018 NIS, combined 2018-2019 Florida and New York State Inpatient Databases (SID), and 2016-2022 institutional data. 699,155 records from the 2019 NIS were used for model development. The CORE score better predicted in-hospital mortality compared to the ECI within the NIS (0.90, 95%CI:0.90-0.90 vs. 0.84, 95%CI:0.84-0.84), SID (0.91, 95%CI:0.90-0.91 vs. 0.86, 95%CI:0.86-0.87), and institutional (0.88, 95%CI:0.87-0.89 vs. 0.84, 95%CI:0.83-0.85) databases (all P<0.001). Likewise, it outperformed the CCI for the NIS (0.76, 95%CI:0.76-0.76), SID (0.78, 95%CI:0.77-0.78), and institutional (0.62, 95%CI:0.60-0.64) cohorts (all P<0.001). The CORE score may better predict in-hospital mortality after surgery due to comorbid diseases in outcome-based research.