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Novel and Innovative Hybrid Technique for Type A Aortic Dissection
Published on: March 28, 2025
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Machine learning-based analysis of factors influencing surgical duration in type A aortic dissection
Dechao Deng1,2,3, Xiaoming Zhang4, Xiangzhen Feng4
1Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.
Frontiers in Public Health
|November 17, 2025
Summary
Machine learning models predict surgical time for Stanford Type A aortic dissection (TAAD). Key factors influencing operative duration include extracorporeal circulation and aortic occlusion times, guiding surgical planning.
Area of Science:
- Cardiovascular Surgery
- Artificial Intelligence in Medicine
- Aortic Disease Research
Background:
- Stanford Type A aortic dissection (TAAD) is a critical condition of the ascending aorta necessitating immediate surgical intervention.
- Predicting and understanding factors influencing operative duration in TAAD surgery is crucial for patient outcomes and resource management.
Purpose of the Study:
- To develop and validate machine learning models for predicting operative duration in TAAD surgery.
- To identify key preoperative and intraoperative clinical factors that significantly impact surgical time.
Main Methods:
- A retrospective cohort study involving 505 patients who underwent TAAD surgery between December 2017 and March 2023.
- Eleven machine learning regression models were constructed using 47 features to predict operative duration, with performance evaluated using R², RMSE, and MAE.
- SHAP analysis was employed to enhance model interpretability and identify critical predictive features.
Main Results:
- Complex aortic procedures, specifically root and total arch replacement, were associated with significantly longer surgical durations.
- The ExtraTrees Regressor model demonstrated the highest predictive accuracy for operative duration.
- SHAP analysis identified duration of extracorporeal circulation, duration of aortic occlusion, intraoperative blood transfusion, and treatment methods for the aortic arch and root as key influencing factors.
Conclusions:
- High-performance predictive models were successfully developed to identify critical determinants of operative duration in TAAD surgery.
- Surgical strategies, particularly complex reconstructions and prolonged aortic occlusion, substantially influence procedural length.
- Findings underscore the importance of considering specific patient subgroups and optimizing intraoperative management to mitigate extended surgical times.
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