Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.

中文翻译：

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基于机器学习的急性A型主动脉夹层手术治疗术后不良后果的预测模型

急性A型主动脉夹层（AAAD）急诊手术后发生术后不良后果（PAO）的可能性很高，因此探索住院期间发生PAO的危险因素是降低术后死亡率、改善预后的关键。采用人工智能方法通过临床数据驱动的机器学习构建 PAO 预测模型，以预测 AAAD 全牙弓修复术后 PAO 的发生率。这项研究包括 380 名 AAAD 患者。使用 LASSO 回归分析选择与 PAO 相关的临床特征。尝试了六种不同的机器学习算法进行建模，并利用受试者工作特征曲线、校准曲线、精度召回曲线和决策分析曲线综合分析每种模型的性能。通过 Shapley 加法解释 (SHAP) 解释最佳模型并执行个性化风险评估。经过综合分析，作者认为极限梯度提升（XGBoost）模型是最优模型，其性能优于其他模型。作者基于XGBoost算法成功构建了AAAD患者PAO的预测模型，并用SHAP方法解释了该模型，有助于早期识别高危AAAD患者，并调整个体化患者相关的临床治疗方案。及时地。