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ORIGINAL RESEARCH article

Front. Public Health

Sec. Digital Public Health

Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1682339

This article is part of the Research TopicExtracting Insights from Digital Public Health Data using Artificial Intelligence, Volume IIIView all 16 articles

Machine learning-based analysis of factors influencing surgical duration in type A aortic dissection

Provisionally accepted
Dechao  DengDechao Deng1Xiaoming  ZhangXiaoming Zhang2Xiangzhen  FengXiangzhen Feng2Gaoli  LiuGaoli Liu2Pingping  WangPingping Wang1Jinyu  CongJinyu Cong1Xiang  LiXiang Li1Kunmeng  LiuKunmeng Liu1*Benzheng  WEIBenzheng WEI1*
  • 1Qingdao Academy of Chinese Medicinal Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, China
  • 2The Affiliated Hospital of Qingdao University, Qingdao, China

The final, formatted version of the article will be published soon.

Background: Stanford Type A aortic dissection (TAAD) is a life-threatening condition involving the ascending aorta and requires urgent surgery. This study developed eleven machine learning regression models to predict operative duration and identify key clinical factors influencing surgical time in TAAD. Material and methods:In this single-center retrospective cohort study of 505 patients who underwent surgery from December 2017 to March 2023. Specifically, eleven machine learning models were construct using 47 preoperative and intraoperative features to predict operative duration. Model performance was assessed by R², RMSE, and MAE, and SHAP analysis enhanced interpretability. Results: The study primarily consisted of middle-aged patients, comprising 73.4% males and 26.6% females. Furthermore, most patients underwent complex aortic procedures under time-constrained preoperative conditions. Procedures involving root replacement and total arch replacement were associated with longer surgical durations. The ExtraTrees Regressor had the highest predictive accuracy. SHAP analysis revealed five key features: Duration of extracorporeal circulation, Duration of aortic occlusion, Intraoperative blood transfusion, Treatment method for the aortic arch, and Treatment method for the aortic root. Conclusion: This study developed high-performance predictive models to identify key features affecting operative duration in TAAD surgery. Complex reconstructions prolong procedures, and longer aortic occlusion further contributes to this effect. The findings highlight the major influence of surgical strategies and intraoperative management on surgical duration. Special consideration remains warranted for specific patient subgroups.

Keywords: Stanford type A aortic dissection, machine learning, Prediction models, surgical duration, Shapley additive explanations

Received: 08 Aug 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Deng, Zhang, Feng, Liu, Wang, Cong, Li, Liu and WEI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Kunmeng Liu, liukunmeng@sdutcm.edu.cn
Benzheng WEI, wbz99@sina.com

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