Luo Hanshen, Liu Xinyi, Yang Yuehang, Tang Bing, He Pan, Ding Li, Wang Zhiwen, Shi Jiawei
Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China.
Department of Cardiovascular Surgery, Beijing Aortic Disease Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China.
Sci Rep. 2025 Jul 1;15(1):20930. doi: 10.1038/s41598-025-06936-4.
A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of improving postoperative survival quality and guiding clinical treatment. We retrospectively analyzed data from 635 ATAAD patients who underwent TAR + FET surgery at our institution between January 2018 and October 2023. Based on the International Aortic Arch Surgery Study Group definition of Major Adverse Outcomes (MAO), the entire dataset was divided into 160 patients with MAO and 475 patients without MAO. We utilized 66 variables to train 190 machine learning models. The SHAP method identified 11 strong predictors to create a simplified model. We evaluated the predictive performance and clinical utility of both models using receiver operating characteristic (ROC) curves, precision-recall curves (PRC), calibration plots, and clinical decision curves. The combination of Random Survival Forest (RSF) and Gradient Boosting Machine (GBM) was identified as the best predictive model. Both the full model and the simplified model achieved an area under the ROC curve above 0.85 and an area under the PRC curve above 0.703. The Brier values for the simplified model's calibration outcomes in the training and validation sets were 0.124 and 0.138, respectively, with a clinical utility risk threshold probability range of 0.2 to 0.9. A web-based simplified prediction model was developed (https://pmodel.shinyapps.io/pmodel/), enabling the prediction of complication risk in ATAAD patients undergoing TAR + FET surgery, thereby guiding clinical treatment decisions. The combination model of RSF and GBM effectively predicts the risk of postoperative complications in ATAAD patients, helping surgeons identify high-risk individuals and implement personalized perioperative management.
开发并验证了一种机器学习模型，用于预测接受全弓置换联合冰冻象鼻术（TAR+FET）的急性A型主动脉夹层（ATAAD）患者的术后并发症，旨在提高术后生存质量并指导临床治疗。我们回顾性分析了2018年1月至2023年10月期间在我院接受TAR+FET手术的635例ATAAD患者的数据。根据国际主动脉弓外科学研究组对主要不良结局（MAO）的定义，将整个数据集分为160例有MAO的患者和475例无MAO的患者。我们利用66个变量训练了190个机器学习模型。SHAP方法识别出11个强预测因子以创建一个简化模型。我们使用受试者操作特征（ROC）曲线、精确召回率曲线（PRC）、校准图和临床决策曲线评估了这两种模型的预测性能和临床效用。随机生存森林（RSF）和梯度提升机（GBM）的组合被确定为最佳预测模型。完整模型和简化模型的ROC曲线下面积均高于0.85，PRC曲线下面积均高于0.703。简化模型在训练集和验证集中校准结果的Brier值分别为0.124和0.138，临床效用风险阈值概率范围为0.2至0.9。开发了一个基于网络的简化预测模型（https://pmodel.shinyapps.io/pmodel/），能够预测接受TAR+FET手术的ATAAD患者的并发症风险，从而指导临床治疗决策。RSF和GBM的组合模型有效地预测了ATAAD患者术后并发症的风险，帮助外科医生识别高危个体并实施个性化的围手术期管理。
