{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T08:04:49Z","timestamp":1792829089609,"version":"4.5.0"},"reference-count":28,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T00:00:00Z","timestamp":1707609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Hypertension"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010driven 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\u2010risk AAAD patients at an early stage and to adjust individual patient\u2010related clinical treatment plans in a timely manner.<\/jats:p>","DOI":"10.1111\/jch.14774","type":"journal-article","created":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T08:40:35Z","timestamp":1707640835000},"page":"251-261","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning"],"prefix":"10.1111","volume":"26","author":[{"given":"Lin\u2010feng","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Surgery Fujian Medical University Union Hospital  Fuzhou Fujian P.R. 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