AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study
Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this study aims to develop a non-invasive, effici...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-94734-3 |
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| author | Shuai Wang Sichen Tao Ying Zhu Qiao Gu Peifeng Ni Weidong Zhang Chenxi Wu Ruihan Zhao Wei Hu Mengyuan Diao |
| author_facet | Shuai Wang Sichen Tao Ying Zhu Qiao Gu Peifeng Ni Weidong Zhang Chenxi Wu Ruihan Zhao Wei Hu Mengyuan Diao |
| author_sort | Shuai Wang |
| collection | DOAJ |
| description | Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this study aims to develop a non-invasive, efficient artificial intelligence (AI)-enabled model to predict the risk of mortality within 28 days post-weaning from VA-ECMO. A multicenter, retrospective cohort study was conducted across five hospitals in China, including all the patients who received VA-ECMO support between January 2020 and January 2024. Based on the innovatively selected 25 easily obtainable patient examination features as potentially relevant, this study involved developing ten predictive models using both classical and advanced machine learning techniques. The model’s performance is evaluated using various statistical metrics and the optimal predictive model are identified. Feature correlations are analyzed using Pearson correlation coefficients, and SHapley Additive exPlanations (SHAP) are employed to interpret feature importance. Decision curve analysis is used to evaluate the clinical utility of the predictive models. The study included 225 patients, with 66 patients from one hospital forming the training cohort. Three validation cohorts were used: internal validation with 16 patients from the training hospital and external validation with 30 and 60 patients from the other 4 hospitals. The random forest model emerged as the best predictor of 28-day mortality, achieving an AUROC of 1.00 in the training cohort and 1.00, 0.97, and 0.93 in the three validation cohorts, respectively. Despite the limited training data, the developed model, eCMoML, demonstrated high accuracy, generalizability and reliability. The model will be available online for immediate use by clinicians. The eCMoML model, validated in a multicenter cohort study, offers a rapid, stable, and accurate tool for predicting 28-day mortality post-VA-ECMO weaning. It has the potential to significantly enhance clinical decision-making, helping doctors better assess patient prognosis, optimize treatment plans, and improve survival rates. |
| format | Article |
| id | doaj-art-b0b8f99708e34361a42b2dd5e1e8ecbb |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b0b8f99708e34361a42b2dd5e1e8ecbb2025-08-20T02:55:35ZengNature PortfolioScientific Reports2045-23222025-03-0115112110.1038/s41598-025-94734-3AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort studyShuai Wang0Sichen Tao1Ying Zhu2Qiao Gu3Peifeng Ni4Weidong Zhang5Chenxi Wu6Ruihan Zhao7Wei Hu8Mengyuan Diao9Department of Critical Care, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityFaculty of Engineering, University of ToyamaDepartment of Critical Care, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care, Zhejiang University of MedicineDepartment of Critical Care, The Fourth School of Clinical Medical, Zhejiang Chinese Medical University, Hangzhou First People’s HospitalDepartment of Critical Care, The Fourth School of Clinical Medical, Zhejiang Chinese Medical University, Hangzhou First People’s HospitalSchool of Mechanical Engineering, Tongji UniversityDepartment of Critical Care, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityDepartment of Critical Care, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake UniversityAbstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this study aims to develop a non-invasive, efficient artificial intelligence (AI)-enabled model to predict the risk of mortality within 28 days post-weaning from VA-ECMO. A multicenter, retrospective cohort study was conducted across five hospitals in China, including all the patients who received VA-ECMO support between January 2020 and January 2024. Based on the innovatively selected 25 easily obtainable patient examination features as potentially relevant, this study involved developing ten predictive models using both classical and advanced machine learning techniques. The model’s performance is evaluated using various statistical metrics and the optimal predictive model are identified. Feature correlations are analyzed using Pearson correlation coefficients, and SHapley Additive exPlanations (SHAP) are employed to interpret feature importance. Decision curve analysis is used to evaluate the clinical utility of the predictive models. The study included 225 patients, with 66 patients from one hospital forming the training cohort. Three validation cohorts were used: internal validation with 16 patients from the training hospital and external validation with 30 and 60 patients from the other 4 hospitals. The random forest model emerged as the best predictor of 28-day mortality, achieving an AUROC of 1.00 in the training cohort and 1.00, 0.97, and 0.93 in the three validation cohorts, respectively. Despite the limited training data, the developed model, eCMoML, demonstrated high accuracy, generalizability and reliability. The model will be available online for immediate use by clinicians. The eCMoML model, validated in a multicenter cohort study, offers a rapid, stable, and accurate tool for predicting 28-day mortality post-VA-ECMO weaning. It has the potential to significantly enhance clinical decision-making, helping doctors better assess patient prognosis, optimize treatment plans, and improve survival rates.https://doi.org/10.1038/s41598-025-94734-3VA-ECMOMedical artificial intelligenceMedical machine learning modelMortality risk prediction |
| spellingShingle | Shuai Wang Sichen Tao Ying Zhu Qiao Gu Peifeng Ni Weidong Zhang Chenxi Wu Ruihan Zhao Wei Hu Mengyuan Diao AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study Scientific Reports VA-ECMO Medical artificial intelligence Medical machine learning model Mortality risk prediction |
| title | AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study |
| title_full | AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study |
| title_fullStr | AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study |
| title_full_unstemmed | AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study |
| title_short | AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study |
| title_sort | ai powered model for predicting mortality risk in va ecmo patients a multicenter cohort study |
| topic | VA-ECMO Medical artificial intelligence Medical machine learning model Mortality risk prediction |
| url | https://doi.org/10.1038/s41598-025-94734-3 |
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