Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.

准确预测术后死亡率不仅对于成功的术后患者护理非常重要，而且对于与患者进行基于信息的共享决策和医疗资源的有效分配也很重要。本研究旨在为非心脏手术后 30 天死亡率创建一个机器学习预测模型，该模型适应可管理数量的临床信息作为输入特征，并针对多中心而非单中心数据进行验证。数据来自四个独立机构的 454,404 名 18 岁以上接受非心脏手术的患者。我们对检索到的数据进行了回顾性分析。只有 12-18 个临床变量用于模型训练。逻辑回归、随机森林分类器、极端梯度提升（XGBoost）、并应用深度神经网络方法来比较预测性能。为了减少过度拟合并创建稳健的模型，执行了具有十倍交叉验证的引导和网格搜索。首尔国立大学医院 (SNUH) 数据中的 XGBoost 方法在受试者工作特征曲线下面积 (AUROC) (0.9376) 和精确召回曲线下面积 (0.1593) 方面表现最佳。使用梨花女子大学医学中心数据 (AUROC, 0.941) 验证 SNUH 模型时，预测性能最好。术前白蛋白、凝血酶原时间和年龄是每个医院模型中最重要的特征。