{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:14:49Z","timestamp":1788308089134,"version":"build-2803163510"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":78,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and\/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal donor-recipient matching remain major challenges and existing donor-recipient matching risk models offer limited predictive accuracy. Our study aims to develop a machine-learning-based model to predict and rank long-term post-transplant survival. We included adult deceased donor liver transplant recipients from the Scientific Registry of Transplant Recipients (2009\u20132019). We developed a first of its kind XGBoost-based survival model to predict graft survival at important discrete timepoints and built a corresponding ranking score. The reliability of the score was compared to existing scores. We included 60,649 waitlist registrants, of which 41,058 (67.7%) were males. We found that our predictive model,\n                    <jats:italic>DisCScore<\/jats:italic>\n                    , was the strongest at predicting donor-recipient pair survival times, with a C-index of 0.858 (95%CI: 0.8525\u20130.8643). We achieved the highest mean time-dependent AUROC at 6-months, 1- and 3- years post-LT using\n                    <jats:italic>DisCScore<\/jats:italic>\n                    (mean AUC (95% CI): 0.797 (0.783\u20130.812), 0.821 (0.809\u20130.832) and 0.865 (0.857\u20130.874) respectively and\n                    <jats:italic>DisCScore<\/jats:italic>\n                    outperformed CoxPH and DeepSurv models in all three timepoints. We found that our model performed best in the high MELD group (MELD\u2009&gt;\u200930) \u2013 where the C-index was the highest: 0.873 (95% CI 0.863\u20130.891), as compared to the low and medium MELD groups. Our proposed model can accurately predict donor-recipient pair survival probabilities, especially in those recipients with high MELD scores. This has the potential to be integrated into the complex decision-making pathway of organ allocation.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-52933-6","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:43:51Z","timestamp":1781606631000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A donor-recipient ranking model to optimize long-term survival post liver transplant"],"prefix":"10.1038","volume":"16","author":[{"given":"Eunice","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingji","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirisha","family":"Rambhatla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamatha","family":"Bhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"52933_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/ti.2022.10263","volume":"35","author":"MT Serrano","year":"2022","unstructured":"Serrano, M. T. et al. Mortality and causes of death after liver transplantation: Analysis of sex differences in a large nationwide cohort. Transpl. Int. 35, 10263 (2022).","journal-title":"Transpl. Int."},{"key":"52933_CR2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ijsu.2020.05.024","volume":"82S","author":"N Goldaracena","year":"2020","unstructured":"Goldaracena, N., Cullen, J. M., Kim, D.-S., Ekser, B. & Halazun, K. J. Expanding the donor pool for liver transplantation with marginal donors. Int. J. Surg. 82S, 30\u201335 (2020).","journal-title":"Int. J. Surg."},{"key":"52933_CR3","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.14336\/AD.2024.0214","volume":"16","author":"Z Xiang","year":"2024","unstructured":"Xiang, Z. et al. Current understanding of marginal grafts in liver transplantation. Aging Dis. 16, 1036\u20131058 (2024).","journal-title":"Aging Dis."},{"key":"52933_CR4","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1002\/lt.24799","volume":"23","author":"A Flores","year":"2017","unstructured":"Flores, A. & Asrani, S. K. The donor risk index: A decade of experience. Liver. Transpl. 23, 1216\u20131225 (2017).","journal-title":"Liver. Transpl."},{"key":"52933_CR5","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1111\/j.1600-6143.2008.02400.x","volume":"8","author":"A Rana","year":"2008","unstructured":"Rana, A. et al. Survival outcomes following liver transplantation (SOFT) score: a novel method to predict patient survival following liver transplantation. Am. J. Transplant 8, 2537\u20132546 (2008).","journal-title":"Am. J. Transplant"},{"key":"52933_CR6","doi-asserted-by":"crossref","unstructured":"Dutkowski, P. et al. Are there better guidelines for allocation in liver transplantation? A novel score targeting justice and utility in the model for end-stage liver disease era. Ann. Surg. 254, 745\u201353; discussion 753 (2011).","DOI":"10.1097\/SLA.0b013e3182365081"},{"key":"52933_CR7","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1111\/j.1600-6143.2008.02491.x","volume":"9","author":"JB Halldorson","year":"2009","unstructured":"Halldorson, J. B., Bakthavatsalam, R., Fix, O., Reyes, J. D. & Perkins, J. D. D-MELD, a simple predictor of post liver transplant mortality for optimization of donor\/recipient matching. Am. J. Transplant. 9, 318\u2013326 (2009).","journal-title":"Am. J. Transplant."},{"key":"52933_CR8","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1111\/ajt.17191","volume":"22","author":"SK Asrani","year":"2022","unstructured":"Asrani, S. K. et al. Assessment of donor quality and risk of graft failure after liver transplantation: The ID2 EAL score. Am. J. Transplant. 22, 2921\u20132930 (2022).","journal-title":"Am. J. Transplant."},{"key":"52933_CR9","unstructured":"Decision Making in Liver Transplantation--Limited Application of the Liver Donor Risk Index (LDRI)."},{"key":"52933_CR10","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1002\/lt.23879","volume":"20","author":"L Mataya","year":"2014","unstructured":"Mataya, L., Aronsohn, A., Thistlethwaite, J. R. Jr. & Friedman Ross, L. Decision making in liver transplantation--Limited application of the liver donor risk index. Liver Transpl. 20, 831\u2013837 (2014).","journal-title":"Liver Transpl."},{"key":"52933_CR11","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.hbpd.2022.03.001","volume":"21","author":"J Brice\u00f1o","year":"2022","unstructured":"Brice\u00f1o, J., Calleja, R. & Herv\u00e1s, C. Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing. Hepatobiliary. Pancreat. Dis. Int. 21, 347\u2013353 (2022).","journal-title":"Hepatobiliary. Pancreat. Dis. 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