{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:18:21Z","timestamp":1783487901229,"version":"3.55.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>An information most relevant to patients when deciding to accept a kidney offer is an estimate of its potential longevity. Using a novel machine learning approach, our aim was to develop a model that can output personalized curves estimating kidney graft and patient longevity to support clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a retrospective cohort study in recipients of a first deceased donor kidney transplant aged \u226560 years between 2000 and 2020, using the United Network for Organ Sharing dataset. Outcomes were overall graft survival and patient survival. Independent variables included the Kidney Donor Risk Index (KDRI) and recipient-related variables. Random survival forest models were trained on 70% and validated on 30% of the dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study cohort included 57, 280 patients, amongst whom 27, 244 (48%) experienced graft loss, while 25, 210 (44%) died during a median follow-up of 4.3 years. The most important variables in tree development for graft survival were recipient age, recipient diabetes, KDRI, recipient ethnicity, and time on dialysis. For patient survival, these variables were recipient age, recipient diabetes, time on dialysis, recipient cause of chronic kidney disease, and recipient HCV status. The difference in expected graft survival between low and high KDRI kidneys decreased when recipient characteristics, in particular age, were accounted for. KDRI had a negligible impact on mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>While implementation studies are needed, the individualized curves provided by our novel machine learning approach have the potential to support shared decision-making to a greater extent than the KDRI alone.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1881830","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:51:58Z","timestamp":1783486318000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel machine learning approach generates personalized estimates of graft and patient survival to better inform older kidney transplant candidates"],"prefix":"10.3389","volume":"17","author":[{"given":"Pierre-Luc","family":"Boivin","sequence":"first","affiliation":[{"name":"Department of Mathematical and Industrial Engineering, Polytechnique Montr\u00e9al","place":["Montreal, QC, Canada"]},{"name":"&CO Collaborateurs Cr\u00e9atifs","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Jalbert","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Industrial Engineering, Polytechnique Montr\u00e9al","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u00e9vis","family":"Th\u00e9riault","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering, Polytechnique Montr\u00e9al","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Qi","sequence":"additional","affiliation":[{"name":"Centre d\u2019Int\u00e9gration et d\u2019Analyse en Donn\u00e9es M\u00e9dicales, Centre de recherche du Centre hospitalier de l\u2019Universit\u00e9 de Montr\u00e9al","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasiya","family":"Olek-Basanets","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Industrial Engineering, Polytechnique Montr\u00e9al","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H\u00e9loise","family":"Cardinal","sequence":"additional","affiliation":[{"name":"Immunopathology Axis, Centre de recherche du Centre hospitalier de l\u2019Universit\u00e9 de Montr\u00e9al","place":["Montreal, QC, Canada"]},{"name":"Department of Internal Medicine (Nephrology), Centre hospitalier de l\u2019Universit\u00e9 de Montr\u00e9al","place":["Montreal, QC, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199912023412303","article-title":"Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant","volume":"341","author":"Wolfe","year":"1999","journal-title":"N Engl J Med"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1038\/ki.1996.307","article-title":"A study of the quality of life and cost-utility of renal transplantation","volume":"50","author":"Laupacis","year":"1996","journal-title":"Kidney Int"},{"key":"B3","volume-title":"Treatment of End-Stage Organ Failure in Canada, Canadian Organ Replacement Register, 2014 to 2023: Kidney Transplants \u2014 Data Tables\u00a02","year":"24"},{"key":"B4","unstructured":"Organ and Procurement and Transplantation Network. 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