Objectives During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavorable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implantation has become a dependable choice for surgical treatment. The objective of this research was to utilize a machine learning technique based on artificial intelligence to detect the factors that increase the risk of mortality within 30 days after surgery in patients who undergo TAR in combination with FET.

Methods From January 2015 to December 2020, a total of 640 patients with ATAAD who underwent TAR and FET were included in this study. The subjects were divided into a test group and a validation group in a random manner, with a ratio of 7 to 3. The objective of our research was to create predictive models by employing different supervised machine learning techniques, such as XGBoost, logistic regression, support vector machine (SVM), and random forest (RF), to assess and compare their respective performances. Furthermore, we employed SHapley Additive exPlanation (SHAP) measures to allocate interpretive attributional values.

Results Among all the patients, 37 (5.78%) experienced perioperative mortality. Subsequently, a total 50 of 10 highly associated variables were selected for model construction. By implementing the new method, the AUC value significantly improved from 0.6981 using the XGBoost model to 0.8687 with the PSO-ELM-FLXGBoost model.

Conclusion In this study, machine learning methods were successfully established to predict ATAAD perioperative mortality, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Approval was obtained (no.2020-1402) from the Ethics Committee of Fuwai hospital. The research involved minimal risk to patients, so patient informed consent was not required. The waiver has no negative impact on the participants' rights and well-being.

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