{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:22:49Z","timestamp":1788355369248,"version":"build-2803163510"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Transplantation"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short\u2010term graft outcomes, chronic rejection impedes comparable progress in long\u2010term graft outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data from the national Standard Transplant Analysis and Research (STAR) quarterly file from 1987 to 2023, provided by the Organ Procurement and Transplantation Network (OPTN), and machine\u2010learning techniques were leveraged to determine novel risk factors for graft failure due to chronic rejection in pediatric kidney transplants. A predictive model was developed in conjunction, based on the performances of six classification models, including logistic regression,\n                      <jats:italic>k<\/jats:italic>\n                      \u2010Nearest Neighbors, Support Vector Machine, Decision Tree, Artificial Neural Network, and Random Forest.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The 19 pre\u2010transplant and at\u2010transplant factors identified include those substantiated in literature, such as living donor type, cold ischemic time, human leukocyte antigen (HLA) matching, recipient age, and recipient race. Other factors include one\u2010haplotype matched transplants, recipient age being &lt;\u20095\u2009years, and the proximities of the most and least recent serum crossmatch tests to transplantation. The latter may correlate with recipient sensitization and socioeconomic disparities, but further research must be done to validate this hypothesis. The Random Forest model was selected based on its performance metrics (AUC 0.81).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This case\u2013control study identifies key factors for chronic rejection\u2010caused graft failure 15\u2009years post\u2010transplant in pediatric kidney transplants and develops a Random Forest predictive model based on these factors. Continued investigation is needed to better understand the variables contributing to pediatric chronic kidney rejection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/petr.70043","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:01:11Z","timestamp":1740121271000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Identifying Risk Factors for Graft Failure due to Chronic Rejection\u2009&lt;\u200915\u2009Years Post\u2010Transplant in Pediatric Kidney Transplants Using Random Forest Machine\u2010Learning Techniques"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8039-2562","authenticated-orcid":false,"given":"Hyewon","family":"Suh","sequence":"first","affiliation":[{"name":"Noble and Greenough School  Dedham Massachusetts 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