Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry
Optimizing lung transplant candidate selection is crucial for maximizing resource efficiency and improving patient outcomes. Using data from the International Society for Heart and Lung Transplantation (ISHLT) registry (29,364 patients), we developed a deep learning model to predict 1-year survival...
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| Format: | Article |
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Frontiers Media S.A.
2025-06-01
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| Series: | Transplant International |
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| Online Access: | https://www.frontierspartnerships.org/articles/10.3389/ti.2025.14121/full |
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| author | Hye Ju Yeo Hye Ju Yeo Dasom Noh Eunjeong Son Eunjeong Son Sunyoung Kwon Sunyoung Kwon Sunyoung Kwon Woo Hyun Cho Woo Hyun Cho |
| author_facet | Hye Ju Yeo Hye Ju Yeo Dasom Noh Eunjeong Son Eunjeong Son Sunyoung Kwon Sunyoung Kwon Sunyoung Kwon Woo Hyun Cho Woo Hyun Cho |
| author_sort | Hye Ju Yeo |
| collection | DOAJ |
| description | Optimizing lung transplant candidate selection is crucial for maximizing resource efficiency and improving patient outcomes. Using data from the International Society for Heart and Lung Transplantation (ISHLT) registry (29,364 patients), we developed a deep learning model to predict 1-year survival after lung transplantation. Initially, 25 pretransplant factors were identified, and their importance was assessed using SHapley Additive exPlanations values. We refined the model by selecting the top 10 most influential factors and compared its performance with the original model. Additionally, we conducted external validation using an independent in-house dataset. Among the 29,364 patients, 4,729 (16.1%) died within 1 year, while 24,635 survived. The Gradient Boosting Machine (GBM) model achieved the highest performance (AUC: 0.958, accuracy: 0.949). Notably, the streamlined model using only the top 10 factors maintained identical performance (AUC: 0.958, accuracy: 0.949). The in-house dataset used for external validation showed significant compositional differences compared to the ISHLT dataset. Despite these differences, the GBM model performed well (AUC: 0.852, accuracy: 0.764). Notably, the Multilayer Perceptron model demonstrated superior generalization with an AUC of 0.911 and accuracy of 0.870. Our machine learning-based approach effectively predicts 1-year mortality in lung transplant recipients using a minimal set of pretransplant factors. |
| format | Article |
| id | doaj-art-783419f6dca64e5ba2a65e0f0e744895 |
| institution | DOAJ |
| issn | 1432-2277 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Transplant International |
| spelling | doaj-art-783419f6dca64e5ba2a65e0f0e7448952025-08-20T03:16:15ZengFrontiers Media S.A.Transplant International1432-22772025-06-013810.3389/ti.2025.1412114121Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT RegistryHye Ju Yeo0Hye Ju Yeo1Dasom Noh2Eunjeong Son3Eunjeong Son4Sunyoung Kwon5Sunyoung Kwon6Sunyoung Kwon7Woo Hyun Cho8Woo Hyun Cho9Transplant Research Center, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Internal Medicine, School of Medicine, Pusan National University, Busan, Republic of KoreaDepartment of Information Convergence Engineering, Pusan National University, Yangsan, Republic of KoreaTransplant Research Center, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Internal Medicine, School of Medicine, Pusan National University, Busan, Republic of KoreaDepartment of Information Convergence Engineering, Pusan National University, Yangsan, Republic of KoreaSchool of Biomedical Convergence Engineering, Pusan National University, Yangsan, Republic of KoreaCenter for Artificial Intelligence Research, Pusan National University, Busan, Republic of KoreaTransplant Research Center, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of KoreaDepartment of Internal Medicine, School of Medicine, Pusan National University, Busan, Republic of KoreaOptimizing lung transplant candidate selection is crucial for maximizing resource efficiency and improving patient outcomes. Using data from the International Society for Heart and Lung Transplantation (ISHLT) registry (29,364 patients), we developed a deep learning model to predict 1-year survival after lung transplantation. Initially, 25 pretransplant factors were identified, and their importance was assessed using SHapley Additive exPlanations values. We refined the model by selecting the top 10 most influential factors and compared its performance with the original model. Additionally, we conducted external validation using an independent in-house dataset. Among the 29,364 patients, 4,729 (16.1%) died within 1 year, while 24,635 survived. The Gradient Boosting Machine (GBM) model achieved the highest performance (AUC: 0.958, accuracy: 0.949). Notably, the streamlined model using only the top 10 factors maintained identical performance (AUC: 0.958, accuracy: 0.949). The in-house dataset used for external validation showed significant compositional differences compared to the ISHLT dataset. Despite these differences, the GBM model performed well (AUC: 0.852, accuracy: 0.764). Notably, the Multilayer Perceptron model demonstrated superior generalization with an AUC of 0.911 and accuracy of 0.870. Our machine learning-based approach effectively predicts 1-year mortality in lung transplant recipients using a minimal set of pretransplant factors.https://www.frontierspartnerships.org/articles/10.3389/ti.2025.14121/fulllung transplantationmortalitymachine learningrisk factorsprediction model |
| spellingShingle | Hye Ju Yeo Hye Ju Yeo Dasom Noh Eunjeong Son Eunjeong Son Sunyoung Kwon Sunyoung Kwon Sunyoung Kwon Woo Hyun Cho Woo Hyun Cho Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry Transplant International lung transplantation mortality machine learning risk factors prediction model |
| title | Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry |
| title_full | Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry |
| title_fullStr | Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry |
| title_full_unstemmed | Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry |
| title_short | Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry |
| title_sort | machine learning for 1 year mortality prediction in lung transplant recipients ishlt registry |
| topic | lung transplantation mortality machine learning risk factors prediction model |
| url | https://www.frontierspartnerships.org/articles/10.3389/ti.2025.14121/full |
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