{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:17:10Z","timestamp":1791951430116,"version":"4.3.4"},"reference-count":23,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Predicting delayed graft function (DGF) relies on donor, recipient, and perioperative factors. Despite the growing recognition that DGF duration strongly influences patient outcomes, no models currently address or predict its impact\u2014highlighting an important gap in current research and clinical practice. This study aimed to develop an ensemble\u2010based machine learning model using perioperative data to predict DGF occurrence and duration. The gradient\u2010boosted decision trees model was trained and validated on 2725 patients, with k\u2010fold cross\u2010validation in an external cohort of 284 patients. Model performance was evaluated based on accuracy, ROC\u2010AUC, and other metrics using R and Python libraries. The binary DGF prediction model achieved a ROC\u2010AUC of 0.77, while the DGF duration classification model had an accuracy of 79.2%. DGF duration AUC values by time interval were 0.76 for 0\u20131 weeks, 0.81 for &gt;1\u20132 weeks, 0.80 for &gt;2\u20133 weeks, and 0.87 for &gt;3 weeks; the overall macro\u2010averaged AUC was 0.81. The mean Brier score for multi\u2010class predictions was 0.14. External validation showed a 78% accuracy for DGF duration prediction. Acute kidney injury (AKI) and donor donation after circulatory death (DCD) status were key DGF predictors. The use of gradient\u2010boosted decision trees (GBDT) improves the prediction of both the likelihood and duration of DGF, addressing a current gap in kidney transplant patient care. By facilitating personalized transplant care, this model supports more effective perioperative planning and timely interventions, which may contribute to better patient outcomes.<\/jats:p>","DOI":"10.1111\/ctr.70514","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:20:22Z","timestamp":1774268422000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Machine Learning to Predict Delayed Graft Function Occurrence and Length in Kidney Transplant Recipients"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5670-2488","authenticated-orcid":false,"given":"Alexandru","family":"Nica","sequence":"first","affiliation":[{"name":"Mayo Clinic Alix School of Medicine  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8282-6820","authenticated-orcid":false,"given":"Mateo Velasquez","family":"Mejia","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery Mayo Clinic  Rochester, Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Abdelrheem","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery Mayo Clinic  Rochester, Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9318-2437","authenticated-orcid":false,"given":"Byron","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Biomedical Statistics and Informatics Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-9515","authenticated-orcid":false,"given":"Walter","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery Mayo Clinic  Rochester, Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1392-0792","authenticated-orcid":false,"given":"Mark","family":"Stegall","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery Mayo Clinic  Rochester, Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0367-2008","authenticated-orcid":false,"given":"Raymond","family":"Heilman","sequence":"additional","affiliation":[{"name":"Division of Nephrology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7860-9519","authenticated-orcid":false,"given":"Caroline","family":"Jadlowiec","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery Mayo Clinic  Phoenix Arizona 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