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

Objective: To create a novel comorbidity score tailored for surgical database research. Summary Background Data: 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. Methods: 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. Results: 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). Conclusions: The CORE score may better predict in-hospital mortality after surgery due to comorbid diseases in outcome-based research.