A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning
© 2024 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC..
Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:26 |
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| Enthalten in: |
Journal of clinical hypertension (Greenwich, Conn.) - 26(2024), 3 vom: 10. März, Seite 251-261 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xie, Lin-Feng [VerfasserIn] |
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| Links: |
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| Themen: |
Acute type A aortic dissection |
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| Anmerkungen: |
Date Completed 08.03.2024 Date Revised 22.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/jch.14774 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368311341 |
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| 520 | |a © 2024 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC. | ||
| 520 | |a Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a acute type A aortic dissection | |
| 650 | 4 | |a artificial intelligence | |
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| 650 | 4 | |a triple-branched stent graft | |
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| 700 | 1 | |a Lin, Xin-Fan |e verfasserin |4 aut | |
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