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. 2025 Dec;57(1):2540018.
doi: 10.1080/07853890.2025.2540018. Epub 2025 Jul 31.

An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation

Shuai Zhang  1 , Lulu Li  1 , Jingyu Wang  2 , Yuan Li  1 , Yongkang Zhou  1 , Yuqing Tao  1 , Cuntao Yu  1 , Xiaogang Sun  1 , Hongwei Guo  1 , Dong Zhao  1 , Yi Chang  1 , Jing Sun  1 , Xiangyang Qian  1
Affiliations

An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation

Shuai Zhang et al. Ann Med. 2025 Dec.

Abstract

Objectives: During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavourable 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.

Keywords: Machine learning (ML); acute type A aortic dissection; mortality; prediction model; stent elephant implantation; total aortic arch replacement.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Study flow chart. Abbreviations: ELM: extreme learning machine; PSO: particle swarm optimization; FL: focal loss.
Figure 2.
Figure 2.
(A) Hot map of the selected variables. (B) The hot map of the top ten variables related to intensity in first stage. Abbreviations: ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CABG: coronary artery bypass grafting; CK-MB: creatine kinase-MB; COPD: chronic obstructive pulmonary disease; CPB: cardiopulmonary bypass; EGFR: estimated glomerular filtration rate; LDH: lactate dehydrogenase; LVD: left ventricular internal diameter; LVEF: left ventricular ejection fraction; LYMPH: lymphocyte; NEUT: neutrophil; NLR: neutrophil to lymphocyte ratio; NYHA: New York heart association classification; PLT: platelets; RBC: red blood cell; TEVAR: thoracic endovascular aortic repair; WBC: white blood cell.
Figure 2.
Figure 2.
(A) Hot map of the selected variables. (B) The hot map of the top ten variables related to intensity in first stage. Abbreviations: ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CABG: coronary artery bypass grafting; CK-MB: creatine kinase-MB; COPD: chronic obstructive pulmonary disease; CPB: cardiopulmonary bypass; EGFR: estimated glomerular filtration rate; LDH: lactate dehydrogenase; LVD: left ventricular internal diameter; LVEF: left ventricular ejection fraction; LYMPH: lymphocyte; NEUT: neutrophil; NLR: neutrophil to lymphocyte ratio; NYHA: New York heart association classification; PLT: platelets; RBC: red blood cell; TEVAR: thoracic endovascular aortic repair; WBC: white blood cell.
Figure 3.
Figure 3.
By implementing new methods, the AUC value significantly improved from 0.6981 using the XGBoost model to 0.8687 with the PSO-ELM-FLXGBoost model. (A) The AUC (0.7514) for the ELM-FLXGBoost model was larger than that for the MF-XGBoost (0.7274) and XGBoost (0.6981). (B) The AUC (0.8687) for the PSO-ELM-FLXGBoost model was largest.
Figure 4.
Figure 4.
Plotting the matrix of importance for the PSO-FLXGBoost technique. The model is influenced by the top 10 variables that are of utmost significance.
Figure 5.
Figure 5.
SHAP analysis of top 10 significant variables.
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