Frontiers in public health · 2025
Machine learning-based analysis of factors influencing surgical duration in type A aortic dissection.
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Deng Dechao, Zhang Xiaoming, Feng Xiangzhen, Liu Gaoli, Wang Pingping, Cong Jinyu, Li Xiang, Liu Kunmeng, Wei Benzheng
Аннотация
BACKGROUND: Stanford Type A aortic dissection (TAAD) is a life-threatening condition involving the ascending aorta and requires urgent surgery. This study developed 11 machine learning regression models to predict operative duration and identify key clinical factors influencing surgical time in TAAD.
MATERIALS AND METHODS: In this single-center retrospective cohort study of 505 patients who underwent surgery from December 2017 to March 2023. Specifically, 11 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.
MATERIALS AND METHODS: In this single-center retrospective cohort study of 505 patients who underwent surgery from December 2017 to March 2023. Specifically, 11 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.