{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:34:49Z","timestamp":1781274889185,"version":"3.54.1"},"reference-count":42,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Hepatology Research"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision\u2010making process for clinical application.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>Data from 5242 donor\u2013recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty\u2010three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1\u2010month and 3\u2010month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model\u2019s decisions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor <jats:italic>\u03b3<\/jats:italic>\u2010glutamyl transpeptidase, and blood glucose levels as top predictors of post\u2010LT DGF. The proposed model AUC\u2019s 1\u2010month survival prediction was 0.841 and the 3\u2010month survival prediction was 0.834.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.<\/jats:p><\/jats:sec>","DOI":"10.1111\/hepr.14187","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T15:08:16Z","timestamp":1743865696000},"page":"908-921","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable machine learning for the assessment of donor grafts in liver transplantation"],"prefix":"10.1111","volume":"55","author":[{"given":"Liang","family":"Zhixing","sequence":"first","affiliation":[{"name":"Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat\u2010sen University  Guangzhou China"},{"name":"Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Linsen","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat\u2010sen University  Guangzhou China"},{"name":"Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat\u2010sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat\u2010sen University  Guangzhou China"},{"name":"Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated 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