{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:17:28Z","timestamp":1788412648782,"version":"build-2803163510"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100022292","name":"UHCW","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022292","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,27]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the currently used models are of limited discriminative or calibration power and there is a critical need to improve the selection process. We aimed to assess the value of various artificial intelligence (AI) algorithms to improve the risk stratification index.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We evaluated pre-transplant variables among 66\u00a0914 live-donor kidney transplants (performed between 1 December 2007 and 1 June 2021) from the United Network of Organ Sharing database, randomized into training (80%) and test (20%) sets. The primary outcome measure was death-censored graft survival. We tested four machine learning models for discrimination [time-dependent concordance index (CTD) and area under the receiver operating characteristic curve (AUC)] and calibration [integrated Brier score (IBS)]. We used decision-curve analysis to assess the potential clinical utility.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Among the models, the deep Cox mixture model showed the best discriminative performance (AUC\u00a0=\u00a00.70, 0.68 and 0.68 at 5, 10 and 13 years post-transplant, respectively). CTD reached 0.70, 0.67 and 0.66 at 5, 10 and 13 years post-transplant. The IBS score was 0.09, indicating good calibration. In comparison, applying the Living Kidney Donor Profile Index (LKDPI) on the same cohort produced a CTD of 0.56 and an AUC of 0.55\u20130.58 only. Decision-curve analysis showed an additional net benefit compared with the LKDPI \u2018treat all\u2019 and \u2018treat none\u2019 approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Our AI-based deep Cox mixture model, termed Live-Donor Kidney Transplant Outcome Prediction, outperforms existing prediction models, including the LKDPI, with the potential to improve decisions for optimum live-donor selection by ranking potential transplant pairs based on graft survival. This model could be adopted to improve the outcomes of paired exchange programs.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ndt\/gfae088","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T02:49:16Z","timestamp":1714445356000},"page":"2088-2099","source":"Crossref","is-referenced-by-count":14,"title":["Live-Donor Kidney Transplant Outcome Prediction (L-TOP) using artificial intelligence"],"prefix":"10.1093","volume":"39","author":[{"given":"Hatem","family":"Ali","sequence":"first","affiliation":[{"name":"Renal Department, University Hospitals of Coventry and Warwickshire , Coventry ,","place":["UK"]},{"name":"Research Centre for Health and Life Sciences, Coventry University , Coventry ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Nephrology, University Hospitals of Mississippi , Mississippi ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miklos Z","family":"Molnar","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Division of Nephrology & Hypertension, University of Utah, Spencer Fox Eccles School of Medicine , Salt Lake City, UT ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tibor","family":"F\u00fcl\u00f6p","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology , , Charleston ,","place":["USA"]},{"name":"Medical University South Carolina , , Charleston ,","place":["USA"]},{"name":"Medicine Service, Ralph H. Johnson VA Medical Center , Charleston, SC ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard","family":"Burke","sequence":"additional","affiliation":[{"name":"Research Centre for Health and Life Sciences, Coventry University , Coventry ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunil","family":"Shroff","sequence":"additional","affiliation":[{"name":"CEO, Xtend.AI, CTO, Medindia.net, Technology Adviser, MOHAN Foundation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Shroff","sequence":"additional","affiliation":[{"name":"CEO, Xtend.AI, CTO, Medindia.net, Technology Adviser, MOHAN Foundation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Briggs","sequence":"additional","affiliation":[{"name":"Histocompatibility and Immunogenetics Laboratory , Birmingham Centre, NHS Blood and Transplant ,","place":["UK"]},{"name":"Institute of Immunology and Immunotherapy, University of Birmingham ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nithya","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Renal Department, University Hospitals of Coventry and Warwickshire , Coventry ,","place":["UK"]},{"name":"Research Centre for Health and Life Sciences, Coventry University , Coventry ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"2024112623561619400_bib1","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.1093\/ndt\/gfq780","article-title":"Costeffectiveness analysis of renal replacement therapy in Austria","volume":"26","author":"Haller","year":"2011","journal-title":"Nephrol Dial Transplant"},{"key":"2024112623561619400_bib2","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.1080\/07853890.2021.1962963","article-title":"Impact of kidney transplantation on functional status","volume":"53","author":"Ali","year":"2021","journal-title":"Ann Med"},{"key":"2024112623561619400_bib3","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1093\/ndt\/gfv378","article-title":"Does pre-emptive transplantation vs. post-start of dialysis transplantation with a kidney from a living donor improve outcomes after transplantation? 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