{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T23:23:48Z","timestamp":1792711428439,"version":"4.5.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["AI Award 2020 Phase 1: AI_AWARD02316"],"award-info":[{"award-number":["AI Award 2020 Phase 1: AI_AWARD02316"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000287","name":"Royal Academy of Engineering","doi-asserted-by":"publisher","award":["Research Fellowship scheme"],"award-info":[{"award-number":["Research Fellowship scheme"]}],"id":[{"id":"10.13039\/501100000287","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretable methods to predict both graft failure and patient death using data from previously accepted kidney transplant offers. Using more than 25 years of transplant outcome data, we train and compare several survival analysis models in single risk settings. In addition, we use post hoc interpretability techniques to clinically validate these models. Neural networks show comparable performance to the Cox proportional hazard model, with concordance of 0.63 and 0.79 for prediction of graft failure and patient death, respectively. Donor and recipient ages, the number of mismatches at DR locus, dialysis type, and primary renal disease appear to be important features for transplant outcome prediction. Owing to their good predictive performance and the clinical relevance of their post hoc interpretation, neural networks represent a promising core component in the construction of future decision support systems for transplant offering.<\/jats:p>","DOI":"10.1038\/s41598-024-66976-0","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:01:34Z","timestamp":1722211294000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models"],"prefix":"10.1038","volume":"14","author":[{"given":"Achille","family":"Sala\u00fcn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Knight","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Wingfield","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"issue":"8","key":"66976_CR1","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1093\/jamia\/ocab068","volume":"28","author":"M Chun","year":"2021","unstructured":"Chun, M. et al. Stroke risk prediction using machine learning: A prospective cohort study of 0.5 million Chinese adults. J. Am. Med. Inform. Assoc. 28(8), 1719\u20131727 (2021).","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"1","key":"66976_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"DW Kim","year":"2019","unstructured":"Kim, D. W. et al. Deep learning-based survival prediction of oral cancer patients. Sci. Rep. 9(1), 1\u201310 (2019).","journal-title":"Sci. Rep."},{"issue":"6","key":"66976_CR3","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/j.jbi.2008.03.005","volume":"41","author":"RS Lin","year":"2008","unstructured":"Lin, R. S., Horn, S. D., Hurdle, J. F. & Goldfarb-Rumyantzev, A. S. Single and multiple time-point prediction models in kidney transplant outcomes. J. Biomed. Inform. 41(6), 944\u2013952 (2008).","journal-title":"J. Biomed. Inform."},{"issue":"2","key":"66976_CR4","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1097\/TP.0b013e3181ac620b","volume":"88","author":"PS Rao","year":"2009","unstructured":"Rao, P. S. et al. A comprehensive risk quantification score for deceased donor kidneys: The kidney donor risk index. Transplantation 88(2), 231\u2013236 (2009).","journal-title":"Transplantation"},{"issue":"1","key":"66976_CR5","doi-asserted-by":"publisher","DOI":"10.1097\/TXD.0000000000000856","volume":"5","author":"AJ Vinson","year":"2019","unstructured":"Vinson, A. J., Kiberd, B. A., Davis, R. B. & Tennankore, K. K. Nonimmunologic donor-recipient pairing, HLA matching, and graft loss in deceased donor kidney transplantation. Transpl. Direct 5(1), e414 (2019).","journal-title":"Transpl. Direct"},{"issue":"2","key":"66976_CR6","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. 34(2), 187\u2013202 (1972).","journal-title":"J. R. Stat. Soc."},{"key":"66976_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J. L. et al. DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 1\u201312 (2018).","journal-title":"BMC Med. Res. Methodol."},{"key":"66976_CR8","doi-asserted-by":"crossref","unstructured":"Lee, C., Zame, W., Yoon, J., Van Der\u00a0Schaar, M.: DeepHit: A deep learning approach to survival analysis with competing risks. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"66976_CR9","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1214\/08-AOAS169","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841\u2013860 (2008).","journal-title":"Ann. Appl. Stat."},{"key":"66976_CR10","unstructured":"NHS Risk Communication Tools. https:\/\/www.odt.nhs.uk\/transplantation\/tools-policies-and-guidance\/risk-communication-tools\/"},{"issue":"5","key":"66976_CR11","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1097\/MAT.0b013e318145b9f7","volume":"53","author":"S Krikov","year":"2007","unstructured":"Krikov, S. et al. Predicting kidney transplant survival using tree-based modeling. ASAIO J. 53(5), 592\u2013600 (2007).","journal-title":"ASAIO J."},{"issue":"8","key":"66976_CR12","doi-asserted-by":"publisher","DOI":"10.2196\/26843","volume":"23","author":"SAA Naqvi","year":"2021","unstructured":"Naqvi, S. A. A., Tennankore, K., Vinson, A., Roy, P. C. & Abidi, S. S. R. Predicting kidney graft survival using machine learning methods: Prediction model development and feature significance analysis study. J. Med. Internet Res. 23(8), e26843 (2021).","journal-title":"J. Med. Internet Res."},{"key":"66976_CR13","unstructured":"Molnar, C.: Interpretable Machine Learning (2020)."},{"key":"66976_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00390-x","volume":"2","author":"A Noack","year":"2021","unstructured":"Noack, A., Ahern, I., Dou, D. & Li, B. An empirical study on the relation between network interpretability and adversarial robustness. SN Comput. Sci. 2, 1\u201313 (2021).","journal-title":"SN Comput. Sci."},{"key":"66976_CR15","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C. Why should i trust you? Explaining the predictions of any classifier, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"66976_CR16","unstructured":"Lundberg, S.M., Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf Process. Syst. 30 (2017)."},{"issue":"1","key":"66976_CR17","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven, D. J. & B\u00fchlmann, P. Missforest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112\u2013118 (2012).","journal-title":"Bioinformatics"},{"issue":"2","key":"66976_CR18","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1017\/pan.2020.49","volume":"30","author":"R Lall","year":"2022","unstructured":"Lall, R. & Robinson, T. The MIDAS touch: Accurate and scalable missing-data imputation with deep learning. Polit. Anal. 30(2), 179\u2013196 (2022).","journal-title":"Polit. Anal."},{"issue":"1","key":"66976_CR19","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: What is it and how does it work?. Int. J. Methods Psychiatr. Res. 20(1), 40\u201349 (2011).","journal-title":"Int. J. Methods Psychiatr. Res."},{"key":"66976_CR20","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s10985-007-9048-y","volume":"13","author":"D Lin","year":"2007","unstructured":"Lin, D. On the Breslow estimator. Lifetime Data Anal. 13, 471\u2013480 (2007).","journal-title":"Lifetime Data Anal."},{"issue":"3","key":"66976_CR21","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1097\/TP.0b013e31823f14d4","volume":"93","author":"CJ Watson","year":"2012","unstructured":"Watson, C. J., Johnson, R. J., Birch, R., Collett, D. & Bradley, J. A. A simplified donor risk index for predicting outcome after deceased donor kidney transplantation. Transplantation 93(3), 314\u2013318 (2012).","journal-title":"Transplantation"},{"issue":"6","key":"66976_CR22","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1097\/TP.0000000000001326","volume":"101","author":"MZ Molnar","year":"2017","unstructured":"Molnar, M. Z. et al. Predictive score for posttransplantation outcomes. Transplantation 101(6), 1353 (2017).","journal-title":"Transplantation"},{"issue":"9868","key":"66976_CR23","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/S0140-6736(12)61685-7","volume":"381","author":"DM Summers","year":"2013","unstructured":"Summers, D. M. et al. Effect of donor age and cold storage time on outcome in recipients of kidneys donated after circulatory death in the UK: A cohort study. Lancet 381(9868), 727\u2013734 (2013).","journal-title":"Lancet"},{"issue":"2","key":"66976_CR24","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1038\/ki.2015.88","volume":"88","author":"DM Summers","year":"2015","unstructured":"Summers, D. M. et al. Kidney donation after circulatory death (DCD): State of the art. Kidney Int. 88(2), 241\u2013249 (2015).","journal-title":"Kidney Int."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-66976-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-66976-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-66976-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:02:41Z","timestamp":1722211361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-66976-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["66976"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-66976-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.08.24.23294535","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]},"assertion":[{"value":"18 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17356"}}