The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p< 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien–Dindo I-II), major morbidity (Clavien–Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61,p< 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.
手术患者的医疗复杂性日益增加，手术风险计算器对于提供高价值、以患者为中心的外科诊疗至关重要。然而，现有模型尚未经过验证能够准确预测妇科肿瘤大手术的风险，且多数模型无法推广至中低收入国家医疗环境。本研究基于国际GO SOAR数据库，开发了一种新型妇科手术后并发症及死亡风险的预测计算器。选取了高收入国家与中低收入国家术前均可便捷获取的15项候选特征，采用机器学习方法与线性回归进行预测建模分析，并通过受试者工作特征曲线下面积评估整体判别性能。在评估轻微并发症（Clavien-Dindo I-II级）、严重并发症（Clavien-Dindo III-V级）及无并发症三组预测准确性时，神经网络模型（AUROC 0.94）显著优于其他模型（p<0.001）。逻辑回归模型在预测死亡率方面优于临床通用的SORT模型（AUROC 0.66对比0.61，p<0.001）。GO SOAR手术风险预测模型是首个经验证适用于妇科手术患者的预测工具。在肿瘤细胞减灭术等重大手术中，精准的风险预测至关重要，因为手术及其相关并发症可能降低患者生活质量并影响长期癌症生存率。该模型仅需常规术前数据且不受医疗资源限制，对缩小全球手术质量差异具有关键意义。
肿瘤(癌症)患者之家