Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble
Abstract A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of improving postoperative survival quality...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-06936-4 |
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| author | Hanshen Luo Xinyi Liu Yuehang Yang Bing Tang Pan He Li Ding Zhiwen Wang Jiawei Shi |
| author_facet | Hanshen Luo Xinyi Liu Yuehang Yang Bing Tang Pan He Li Ding Zhiwen Wang Jiawei Shi |
| author_sort | Hanshen Luo |
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| description | Abstract A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of improving postoperative survival quality and guiding clinical treatment. We retrospectively analyzed data from 635 ATAAD patients who underwent TAR + FET surgery at our institution between January 2018 and October 2023. Based on the International Aortic Arch Surgery Study Group definition of Major Adverse Outcomes (MAO), the entire dataset was divided into 160 patients with MAO and 475 patients without MAO. We utilized 66 variables to train 190 machine learning models. The SHAP method identified 11 strong predictors to create a simplified model. We evaluated the predictive performance and clinical utility of both models using receiver operating characteristic (ROC) curves, precision-recall curves (PRC), calibration plots, and clinical decision curves. The combination of Random Survival Forest (RSF) and Gradient Boosting Machine (GBM) was identified as the best predictive model. Both the full model and the simplified model achieved an area under the ROC curve above 0.85 and an area under the PRC curve above 0.703. The Brier values for the simplified model’s calibration outcomes in the training and validation sets were 0.124 and 0.138, respectively, with a clinical utility risk threshold probability range of 0.2 to 0.9. A web-based simplified prediction model was developed (https://pmodel.shinyapps.io/pmodel/), enabling the prediction of complication risk in ATAAD patients undergoing TAR + FET surgery, thereby guiding clinical treatment decisions. The combination model of RSF and GBM effectively predicts the risk of postoperative complications in ATAAD patients, helping surgeons identify high-risk individuals and implement personalized perioperative management. |
| format | Article |
| id | doaj-art-1e498ec2af874ebebc1cd5390ced9a23 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-1e498ec2af874ebebc1cd5390ced9a232025-08-20T04:01:25ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-06936-4Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensembleHanshen Luo0Xinyi Liu1Yuehang Yang2Bing Tang3Pan He4Li Ding5Zhiwen Wang6Jiawei Shi7Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Cardiovascular Surgery, Beijing Aortic Disease Center, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Cardiovascular Surgery, Beijing Aortic Disease Center, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Cardiothoracic Surgery, Henan Provincial Chest Hospital (Chest Hospital of Zhengzhou University)Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of improving postoperative survival quality and guiding clinical treatment. We retrospectively analyzed data from 635 ATAAD patients who underwent TAR + FET surgery at our institution between January 2018 and October 2023. Based on the International Aortic Arch Surgery Study Group definition of Major Adverse Outcomes (MAO), the entire dataset was divided into 160 patients with MAO and 475 patients without MAO. We utilized 66 variables to train 190 machine learning models. The SHAP method identified 11 strong predictors to create a simplified model. We evaluated the predictive performance and clinical utility of both models using receiver operating characteristic (ROC) curves, precision-recall curves (PRC), calibration plots, and clinical decision curves. The combination of Random Survival Forest (RSF) and Gradient Boosting Machine (GBM) was identified as the best predictive model. Both the full model and the simplified model achieved an area under the ROC curve above 0.85 and an area under the PRC curve above 0.703. The Brier values for the simplified model’s calibration outcomes in the training and validation sets were 0.124 and 0.138, respectively, with a clinical utility risk threshold probability range of 0.2 to 0.9. A web-based simplified prediction model was developed (https://pmodel.shinyapps.io/pmodel/), enabling the prediction of complication risk in ATAAD patients undergoing TAR + FET surgery, thereby guiding clinical treatment decisions. The combination model of RSF and GBM effectively predicts the risk of postoperative complications in ATAAD patients, helping surgeons identify high-risk individuals and implement personalized perioperative management.https://doi.org/10.1038/s41598-025-06936-4Type A aortic dissectionPostoperative complicationsMachine learningPredictive modelMajor adverse outcome |
| spellingShingle | Hanshen Luo Xinyi Liu Yuehang Yang Bing Tang Pan He Li Ding Zhiwen Wang Jiawei Shi Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble Scientific Reports Type A aortic dissection Postoperative complications Machine learning Predictive model Major adverse outcome |
| title | Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble |
| title_full | Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble |
| title_fullStr | Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble |
| title_full_unstemmed | Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble |
| title_short | Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble |
| title_sort | preoperative prediction of major adverse outcomes after total arch replacement in acute type a aortic dissection based on machine learning ensemble |
| topic | Type A aortic dissection Postoperative complications Machine learning Predictive model Major adverse outcome |
| url | https://doi.org/10.1038/s41598-025-06936-4 |
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