Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry
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| Natura: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867012714159669249 |
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| author | Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo |
| author_facet | Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo |
| collection | Wiley Open Access |
| contents | Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo Clinical Transplantation ABSTRACT BACKGROUND In regions with limited donor availability, optimizing efficiency in lung transplant decision‐making is crucial. Preoperative prediction of 1‐year graft failure can enhance candidate selection and clinical decision‐making. METHODS We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning‐based model for predicting 1‐year graft failure after lung transplantation. A total of 240 cases were analyzed using 5‐fold cross‐validation. Among 25 preoperative factors associated with 1‐year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development. RESULTS Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733. CONCLUSIONS Our machine learning‐based model effectively predicts 1‐year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability. 10.1111/ctr.70268 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1111/ctr.70268 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ctr_70268 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo Clinical Transplantation Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry Dasom Noh Sunyoung Kwon Woo Hyun Cho Jin Gu Lee Song Yee Kim Samina Park Kyeongman Jeon Hye Ju Yeo Clinical Transplantation ABSTRACT BACKGROUND In regions with limited donor availability, optimizing efficiency in lung transplant decision‐making is crucial. Preoperative prediction of 1‐year graft failure can enhance candidate selection and clinical decision‐making. METHODS We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning‐based model for predicting 1‐year graft failure after lung transplantation. A total of 240 cases were analyzed using 5‐fold cross‐validation. Among 25 preoperative factors associated with 1‐year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development. RESULTS Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733. CONCLUSIONS Our machine learning‐based model effectively predicts 1‐year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability. 10.1111/ctr.70268 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry |
| topic | Clinical Transplantation |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ctr.70268 |