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

To create a novel comorbidity score tailored for surgical database research.

Despite their use in surgical research, the Elixhauser (ECI) and Charlson (CCI) Comorbidity Indices were developed nearly 4 decades ago utilizing primarily nonsurgical cohorts.

Adults undergoing 62 operations across 14 specialties were queried from the 2019 National Inpatient Sample (NIS), using the International Classification of Diseases, 10th Revision codes. International Classification of Diseases, 10th Revision codes for chronic diseases were sorted into Clinical Classifications Software Refined groups. Clinical Classifications Software Refined with non-zero feature importance across 4 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 with 95% CIs 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.

A total of 699,155 records from the 2019 NIS were used for model development. The CORE score better predicted in-hospital mortality compared with 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.