Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results

作者：Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji · 发表于：Reviews in Cardiovascular Medicine · 年份：2025 · DOI：10.31083/rcm37827 · 被引用次数：3 · 研究领域：Aortic Disease and Treatment Approaches、Aortic aneurysm repair treatments、Sepsis Diagnosis and Treatment

Background: Mesenteric malperfusion (MMP) represents a severe complication of acute aortic dissection (AAD). Research on risk identification models for MMP is currently limited. Methods: Based on a retrospective study of medical records from the Beijing Anzhen Hospital spanning from January 2016 to June 2022, we included 435 patients with AAD and allocated their data to training and testing sets at a ratio of 7:3. Key preoperative predictive variables were identified through the least absolute shrinkage and selection operator (LASSO) regression. Subsequently, six machine learning algorithms were used to develop and validate an MMP risk identification model: logistic regression (LR), support vector classification (SVC), random forest (RF), extreme gradient boosting (XGBoost), naive Bayes (NB), and multilayer perceptron (MLP). To determine the optimal model, the performance of the model was evaluated using various metrics, including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and the Brier score. Results: = 0.008) were identified as independent risk factors for MMP. Among the six assessed machine learning algorithms, the RF model exhibited the best predictive capabilities, yielding AUROCs of 0.888 (95% CI 0.887, 0.889) and 0.797 (95% CI 0.794, 0.800) in the training and testing datasets, respectively, as well as sensitivities of 0.864 (95% CI 0.862, 0.867) and 0.811 (95% CI 0.806, 0.816), respectively, in the correspon...