{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:17:09Z","timestamp":1791951429514,"version":"4.3.4"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor\u2019s eGFR, recipient\u2019s BMI, donor\u2019s BMI, and recipient\u2013donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor\u2019s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.<\/jats:p>","DOI":"10.3390\/jcm10225244","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:02:41Z","timestamp":1636671761000},"page":"5244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Artificial Intelligence\u2014A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4966-9771","authenticated-orcid":false,"given":"Andrzej","family":"Konieczny","sequence":"first","affiliation":[{"name":"Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, 50-556 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0527-2190","authenticated-orcid":false,"given":"Jakub","family":"Stojanowski","sequence":"additional","affiliation":[{"name":"Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, 50-556 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaudia","family":"Rydzy\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, 50-556 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariusz","family":"Kusztal","sequence":"additional","affiliation":[{"name":"Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, 50-556 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2632-2409","authenticated-orcid":false,"given":"Magdalena","family":"Krajewska","sequence":"additional","affiliation":[{"name":"Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, 50-556 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1111\/j.1600-6143.2011.03754.x","article-title":"Delayed graft function in the kidney transplant","volume":"11","author":"Siedlecki","year":"2011","journal-title":"Am. J. Transplant."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1111\/ctr.12832","article-title":"Prediction, prevention, and management of delayed graft function: Where are we now?","volume":"30","author":"Nashan","year":"2016","journal-title":"Clin. Transpl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1590\/2175-8239-jbn-2018-0065","article-title":"Long-term effects of delayed graft function duration on function and survival of deceased donor kidney transplants","volume":"41","author":"Helfer","year":"2019","journal-title":"J. Bras. Nefrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"813","DOI":"10.2215\/CJN.13711119","article-title":"Donor age, cold ischemia time, and delayed graft function","volume":"15","author":"Ibrahim","year":"2020","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1097\/00007890-200211150-00014","article-title":"Donor characteristics associated with reduced graft survival: An approach to expanding the pool of kidney donors","volume":"74","author":"Port","year":"2002","journal-title":"Transplantation"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1111\/j.1600-6143.2010.03179.x","article-title":"A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation","volume":"10","author":"Irish","year":"2010","journal-title":"Am. J. Transpl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1147\/rd.33.0210","article-title":"Some studies in machine learning using the game of checkers","volume":"3","author":"Samuel","year":"1959","journal-title":"IBM J. Res. Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1097\/ALN.0000000000002694","article-title":"Artificial intelligence and machine learning in anesthesiology","volume":"131","author":"Connor","year":"2019","journal-title":"Anesthesiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.path.2014.04.002","article-title":"Donor kidney evaluation","volume":"7","author":"Mohamed","year":"2014","journal-title":"Surg. Pathol. Clin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1373\/clinchem.2006.077180","article-title":"Expressing the modification of diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values","volume":"53","author":"Levey","year":"2007","journal-title":"Clin. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1097\/TP.0b013e3181ac620b","article-title":"A comprehensive risk quantification score for deceased donor kidneys: The kidney donor risk index","volume":"88","author":"Rao","year":"2009","journal-title":"Transplantation"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100980","DOI":"10.1016\/j.eclinm.2021.100980","article-title":"US deceased kidney transplantation: Estimated GFR, donor age and KDPI association with graft survival","volume":"37","author":"Pruett","year":"2021","journal-title":"EClinicalMedicine"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e13190","DOI":"10.1111\/ctr.13190","article-title":"The impact of kidney donor profile index on delayed graft function and transplant outcomes: A single-center analysis","volume":"32","author":"Zens","year":"2018","journal-title":"Clin. Transpl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17109","DOI":"10.1038\/s41598-020-74295-3","article-title":"Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients","volume":"10","author":"Coca","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Konieczny, A., Stojanowski, J., Krajewska, M., and Kusztal, M. (2021). Machine learning in prediction of IgA nephropathy outcome: A comparative approach. J. Pers. Med., 11.","DOI":"10.3390\/jpm11040312"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hassine, K., Erbad, A., and Hamila, R. (2019, January 24\u201328). Important complexity reduction of random forest in multi-classification problem. Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco.","DOI":"10.1109\/IWCMC.2019.8766544"},{"key":"ref_17","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","unstructured":"Mizutani, E., and Dreyfus, S.E. (2001, January 15\u201319). On complexity analysis of supervised MLP-learning for algorithmic comparisons. Proceedings of the IJCNN\u201901. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222), Washington, DC, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1034\/j.1600-6143.3.s4.11.x","article-title":"Expanded criteria donors for kidney transplantation","volume":"3","author":"Metzger","year":"2003","journal-title":"Am. J. Transpl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Costa, S.D., de Andrade, L.G.M., Barroso, F.V.C., Oliveira, C.M.C.d., Daher, E.D.F., Fernandes, P.F.C.B.C., Esmeraldo, R.d.M., and Sandes-Freitas, T.V.d. (2020). The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0228597"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18409","DOI":"10.1038\/s41598-020-75473-z","article-title":"Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning","volume":"10","author":"Kawakita","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Decruyenaere, A., Decruyenaere, P., Peeters, P., Vermassen, F., Dhaene, T., and Couckuyt, I. (2015). Prediction of delayed graft function after kidney transplantation: Comparison between logistic regression and machine learning methods. BMC Med. Inform. Decis. Mak., 15.","DOI":"10.1186\/s12911-015-0206-y"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jen, K.Y., Albahra, S., Yen, F., Sageshima, J., Chen, L.X., Tran, N., and Rashidi, H.H. (2021). Automated en masse machine learning model generation shows comparable performance as classic regression models for predicting delayed graft function in renal allografts. Transplantation.","DOI":"10.1097\/TP.0000000000003640"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20190054","DOI":"10.1098\/rsta.2019.0054","article-title":"Machine learning and big scientific data","volume":"378","author":"Hey","year":"2020","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/ki.2014.304","article-title":"Each additional hour of cold ischemia time significantly increases the risk of graft failure and mortality following renal transplantation","volume":"87","author":"Debout","year":"2015","journal-title":"Kidney Int."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.transproceed.2019.11.025","article-title":"Shorter cold ischemia time in deceased donor kidney transplantation reduces the incidence of delayed graft function especially among highly sensitized patients and kidneys from older donors","volume":"52","author":"Lauronen","year":"2020","journal-title":"Transpl. Proc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1097\/TP.0000000000002309","article-title":"The relationships between cold ischemia time, kidney transplant length of Stay, and transplant-related costs","volume":"103","author":"Serrano","year":"2019","journal-title":"Transplantation"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/22\/5244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:28:40Z","timestamp":1760167720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/10\/22\/5244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,11]]},"references-count":27,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["jcm10225244"],"URL":"https:\/\/doi.org\/10.3390\/jcm10225244","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,11]]}}}