{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:12:56Z","timestamp":1788441176905,"version":"build-2803163510"},"reference-count":23,"publisher":"IMR Press","issue":"8","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Smart Medical Care of Zhongshan Hospital","award":["2020ZHZS01"],"award-info":[{"award-number":["2020ZHZS01"]}]},{"name":"The Science and Technology Commission of Shanghai Municipality","award":["20DZ2261200"],"award-info":[{"award-number":["20DZ2261200"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82070085"],"award-info":[{"award-number":["82070085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Rev. Cardiovasc. Med."],"abstract":"<jats:p>Backgrounds:Scoring systems are increasingly used for risk stratification in the postoperative management of type A aortic dissection (TAAD), but no consensus exists regarding the optimal tool. Thus, this study aimed to develop and validate a machine learning-based model to predict in-hospital mortality among postoperative patients with TAAD.Methods:Data from postoperative TAAD patients treated at two centers were analyzed retrospectively. The primary outcome was in-hospital all-cause mortality. Clustering methods were implemented for feature selection. A random forest algorithm was selected from a range of machine learning methods to develop a postoperative mortality risk prediction tool. Receiver operating characteristic (ROC) curves were employed to assess predictive accuracy and reliability. Model calibration was evaluated by plotting calibration curves. SHapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each feature. Model performance was compared with the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II).Results:A total of 983 patients who underwent surgical repair for TAAD were included in the study. Eight features were identified as significant predictors of in-hospital mortality. On postoperative day (POD) 1, the final random forest model achieved an area under the receiver operating characteristic curve (AUROC) of 0.8899 (95% confidence interval (CI): 0.7988\u20130.9469). External validation on POD 1 yielded an AUROC of 0.8331 (95% CI: 0.7722\u20130.8871). The model demonstrated significantly higher discriminative performance than EuroSCORE II.Conclusions:This study developed and primarily validated an accurate machine learning model to predict the all-cause in-hospital mortality of patients with TAAD undergoing surgical repair.<\/jats:p>","DOI":"10.31083\/rcm49025","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T12:17:46Z","timestamp":1788437866000},"source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning Prediction Model for In-Hospital Mortality in Postoperative Patients With Type A Aortic Dissection"],"prefix":"10.31083","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3109-7883","authenticated-orcid":true,"given":"Ming-hao","family":"Luo","sequence":"first","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"},{"name":"Shanghai Medical College, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7070-9074","authenticated-orcid":true,"given":"Xian-tong","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Surgery, First Affiliated Hospital of Xi\u2019an Jiaotong University, 710063 Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin-rui","family":"Huang","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"},{"name":"Shanghai Medical College, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9066-398X","authenticated-orcid":true,"given":"Fei-bai","family":"Yao","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"},{"name":"Shanghai Medical College, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7861-7631","authenticated-orcid":true,"given":"Guo-wei","family":"Tu","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Su","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-yi","family":"Hou","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3979-3745","authenticated-orcid":true,"given":"Cong","family":"Tian","sequence":"additional","affiliation":[{"name":"Philips Research China, 201103 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1559-9527","authenticated-orcid":true,"given":"Yana","family":"Yuan","sequence":"additional","affiliation":[{"name":"Philips Research China, 201103 Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Surgery, First Affiliated Hospital of Xi\u2019an Jiaotong University, 710063 Xi\u2019an, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1384-1565","authenticated-orcid":true,"given":"Zhe","family":"Luo","sequence":"additional","affiliation":[{"name":"Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, 200437 Shanghai, China"},{"name":"Shanghai Key Laboratory of Lung Inflammation and Injury, 200032 Shanghai, China"},{"name":"Department of Critical Care Medicine, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, 200437 Shanghai, China"},{"name":"Key Laboratory of Multiple Organ Failure (Zhejiang University), Ministry of Education, 310058 Hangzhou, Zhejiang, 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Presentation, Diagnosis, and Outcomes of Acute Aortic Dissection: 17-Year Trends From the International Registry of Acute Aortic Dissection. Journal of the American College of Cardiology. 2015; 66: 350\u2013358.","DOI":"10.1016\/j.jacc.2015.05.029"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"Conzelmann LO, Weigang E, Mehlhorn U, Abugameh A, Hoffmann I, Blettner M, et al. Mortality in patients with acute aortic dissection type A: analysis of pre- and intraoperative risk factors from the German Registry for Acute Aortic Dissection Type A (GERAADA). European Journal of Cardio-Thoracic Surgery. 2016; 49: e44\u2013e52.","DOI":"10.1093\/ejcts\/ezv356"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"Nashef SAM, Roques F, Sharples LD, Nilsson J, Smith C, Goldstone AR, et al. EuroSCORE II. 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On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Medicine. 1996; 22: 707\u2013710.","DOI":"10.1007\/BF01709751"},{"key":"ref11","unstructured":"Chan HY, Liu M. Interpretable Sub-phenotype Identification in Acute Kidney Injury. AMIA Annual Symposium Proceedings. AMIA Symposium. 2023; 2022: 339\u2013348."},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"Spencer APC, Goodfellow M. Using deep clustering to improve fMRI dynamic functional connectivity analysis. NeuroImage. 2022; 257: 119288.","DOI":"10.1016\/j.neuroimage.2022.119288"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"Wu W, Ma X. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data. Bioinformatics. 2020; 36: 3825\u20133832.","DOI":"10.1093\/bioinformatics\/btaa231"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"Soussi S, Sharma D, J\u00fcni P, Lebovic G, Brochard L, Marshall JC, et al. Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort. Critical Care. 2022; 26: 114.","DOI":"10.1186\/s13054-022-03972-8"},{"key":"ref15","doi-asserted-by":"publisher","unstructured":"Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016; 315: 762\u2013774.","DOI":"10.1001\/jama.2016.0288"},{"key":"ref16","doi-asserted-by":"publisher","unstructured":"Luo MH, Luo JC, Zhang YJ, Xu X, Su Y, Li JK, et al. Early postoperative organ dysfunction is highly associated with the mortality risk of patients with type A aortic dissection. Interactive Cardiovascular and Thoracic Surgery. 2022; 35: ivac266.","DOI":"10.1093\/icvts\/ivac266"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"Huo Y, Zhang H, Li B, Zhang K, Li B, Guo SH, et al. Risk Factors for Postoperative Mortality in Patients with Acute Stanford Type A Aortic Dissection. International Journal of General Medicine. 2021; 14: 7007\u20137015.","DOI":"10.2147\/IJGM.S330325"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"Margraf A, Ludwig N, Zarbock A, Rossaint J. Systemic Inflammatory Response Syndrome After Surgery: Mechanisms and Protection. Anesthesia and Analgesia. 2020; 131: 1693\u20131707.","DOI":"10.1213\/ANE.0000000000005175"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"Corral-Velez V, Lopez-Delgado JC, Betancur-Zambrano NL, Lopez-Su\u00f1e N, Rojas-Lora M, Torrado H, et al. The inflammatory response in cardiac surgery: an overview of the pathophysiology and clinical implications. Inflammation & Allergy Drug Targets. 2015; 13: 367\u2013370.","DOI":"10.2174\/1871528114666150529120801"},{"key":"ref20","doi-asserted-by":"publisher","unstructured":"Eghbalzadeh K, Sabashnikov A, Weber C, Zeriouh M, Djordjevic I, Merkle J, et al. Impact of preoperative elevated serum creatinine on long-term outcome of patients undergoing aortic repair with Stanford A dissection: a retrospective matched pair analysis. Therapeutic Advances in Cardiovascular Disease. 2018; 12: 289\u2013298.","DOI":"10.1177\/1753944718798345"},{"key":"ref21","doi-asserted-by":"publisher","unstructured":"Bayram M, Duman ZM, Timur B, Ya\u015far E, \u00dcst\u00fcn\u0131\u015f\u0131k \u00c7T, Kaplan MC, et al. Predictive value of age, creatinine, and ejection fraction (ACEF) scoring system for operative mortality in patients with Stanford type A aortic dissection. Indian Journal of Thoracic and Cardiovascular Surgery. 2023; 39: 6\u201313.","DOI":"10.1007\/s12055-022-01431-1"},{"key":"ref22","doi-asserted-by":"publisher","unstructured":"Huang M, Lian Y, Zeng Z, Li J. D-dimer, C-reactive protein and matrix metalloproteinase 9 for prediction of type A aortic dissection patient survival. ESC Heart Failure. 2024; 11: 147\u2013154.","DOI":"10.1002\/ehf2.14552"},{"key":"ref23","doi-asserted-by":"publisher","unstructured":"Nezic D. GERAADA score for the prediction of mortality rate in acute type A aortic dissection surgery. 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