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.

中文翻译：

---

制定用于管理数据的手术特异性合并症评分。


目的 创建一个专为外科数据库研究定制的新颖的合并症评分。摘要背景数据 尽管 Elixhauser (ECI) 和 Charlson 合并症指数 (CCI) 用于外科研究，但它们是在近四十年前主要利用非外科队列开发的。方法 使用国际疾病分类第 10 次修订版 (ICD-10) 代码从 2019 年全国住院患者样本 (NIS) 中对 14 个专科接受 62 次手术的成人进行查询。慢性病的 ICD-10 代码被分为临床分类软件精炼 (CCSR) 组。使用四种预测院内死亡率的机器学习算法中具有非零特征重要性的 CCSR 进行逻辑回归；所得系数用于根据先前验证的方法计算共病手术风险评估 (CORE) 评分。使用具有 95% 置信区间 (CI) 的受试者工作特征 (AUROC) 下的面积来比较模型在预测 CORE 评分、ECI 和 CCI 院内死亡率方面的性能。使用 2016-2018 年 NIS、结合 2018-2019 年佛罗里达州和纽约州住院数据库 (SID) 以及 2016-2022 年机构数据进行验证。结果 2019 年 NIS 中的 699,155 条记录用于模型开发。与 NIS 内的 ECI（0.90，95%CI：0.90-0.90 对比 0.84，95%CI：0.84-0.84）、SID（0.91，95%CI：0.90-0.91）相比，CORE 评分可以更好地预测院内死亡率与 0.86、95%CI：0.86-0.87）和机构数据库（0.88、95%CI：0.87-0.89 与 0.84、95%CI：0.83-0.85）数据库（均为 P<0.001）。同样，它的 CCI 优于 NIS（0.76，95%CI：0.76-0.76）、SID（0.78，95%CI：0.77-0.78）和机构（0.62，95%CI：0.60-0.64）队列（所有P<0.001）。 结论 在基于结果的研究中，CORE 评分可以更好地预测手术后因合并症引起的院内死亡率。