An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation
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 t...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2540018 |
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| Summary: | 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. |
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| ISSN: | 0785-3890 1365-2060 |