{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:34:06Z","timestamp":1771036446954,"version":"3.50.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Timely and efficient allocation of deceased donor kidneys is a persistent challenge in transplantation. Traditional sequential offer systems often lead to extended delays and high nonuse rates, as many kidneys undergo multiple refusals before being accepted. Simultaneously expiring offers, where a kidney is offered to a batch of centers with synchronized response deadlines, offer a more efficient alternative. However, fixed batch sizes fail to account for variability in offer requirements, potentially introducing new inefficiencies or overwhelming transplant professionals with excessive notifications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We investigated the use of machine learning-based survival models to dynamically predict the number of offers a kidney will require before acceptance. Utilizing data on over 16,000 deceased donor kidneys from the national organ offer dataset, we engineered predictive features from both donor profiles and recipient pool characteristics. We trained and evaluated multiple survival models using time-dependent concordance indices along with other survival and regression performance metrics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The Random Survival Forest model achieved the best performance, with a time-dependent C-index of 0.882, effectively estimating the required offer volume for kidney placement. Feature importance analysis revealed that waitlist characteristics, such as mean Estimated Post-Transplant Survival (EPTS), mean Calculated Panel Reactive Antibody (CPRA), time on dialysis, and waitlist duration, were among the most influential predictors. When integrated into a dynamic simultaneous offer system, these predictions have the potential to reduce average placement delays from 17.37 h to 1.59 h while maintaining a manageable level of extraneous offers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our results demonstrate that survival-based predictive modeling can meaningfully improve the efficiency of simultaneously expiring offers in kidney allocation. By personalizing batch sizes based on expected offer burden, such models can reduce delays without overwhelming transplant professionals. These findings underscore the value of integrating real-time, data-driven tools into organ allocation systems to improve operational efficiency and facilitate practical implementation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2025.1662960","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T15:04:07Z","timestamp":1758207847000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers"],"prefix":"10.3389","volume":"8","author":[{"given":"Sean","family":"Berry","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berk","family":"G\u00f6rg\u00fcl\u00fc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sait","family":"Tun\u00e7","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mucahit","family":"Cevik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1111\/ajt.16441","article-title":"Greater complexity and monitoring of the new kidney allocation system: implications and unintended consequences of concentric circle kidney allocation on network complexity","volume":"21","author":"Adler","year":"2021","journal-title":"Am. J. Transplant"},{"key":"B2","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s40472-024-00454-4","article-title":"Use of predictive models to determine transplant eligibility","volume":"11","author":"Berchuck","year":"2024","journal-title":"Curr. Transplant. Rep"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.09116","article-title":"Optimizing hard-to-place kidney allocation: a machine learning approach to center ranking","author":"Berry","year":"2024","journal-title":"arXiv preprint arXiv:2410.09116"},{"key":"B4","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.healthpol.2020.03.012","article-title":"Behavioural economics and human decision making: instances from the health care system","volume":"124","author":"Carminati","year":"2020","journal-title":"Health Policy"},{"key":"B5","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1097\/TP.0000000000003424","article-title":"The future role of machine learning in clinical transplantation","volume":"105","author":"Connor","year":"2021","journal-title":"Transplantation"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1111\/ajt.16161","article-title":"Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: importance of parenchymal injury but not disease activity","volume":"21","author":"Einecke","year":"2021","journal-title":"Am. J. Transplant"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1109\/WSC57314.2022.10015431","author":"Erazo","year":"2022"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-025-02951-7","article-title":"Survival analysis using machine learning in transplantation: a practical introduction","volume":"25","author":"Garcia-Lopez","year":"2025","journal-title":"BMC Med. Inform. Decis. Mak"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.1016\/j.ajt.2023.08.022","article-title":"Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using expert-augmented machine learning","volume":"23","author":"Ge","year":"2023","journal-title":"Am. J. Transplant"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44174-024-00262-5","article-title":"Interpretable machine learning for chronic kidney disease prediction: a SHAP and genetic algorithm-based approach","volume":"3","author":"Gogoi","year":"2024","journal-title":"Biomed. Mater. Devices"},{"key":"B11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41746-022-00637-2","article-title":"The promise of machine learning applications in solid organ transplantation","volume":"5","author":"Gotlieb","year":"2022","journal-title":"NPJ Digit. Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1016\/j.ajt.2025.01.040","article-title":"The dynamics of deceased donor kidney transplant decision-making: Insights from studying individual clinicians' offer decisions","volume":"25","author":"Green","year":"2025","journal-title":"Am. J. Transplant"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e003033","DOI":"10.1136\/fmch-2024-003033","article-title":"Clinical decision fatigue: a systematic and scoping review with meta-synthesis","volume":"13","author":"Grignoli","year":"2025","journal-title":"Fam. Med. Community Health"},{"key":"B14","doi-asserted-by":"publisher","first-page":"S490","DOI":"10.1016\/j.ajt.2025.01.026","article-title":"OPTN\/SRTR 2023 annual data report: deceased organ donation","volume":"25","author":"Israni","year":"2025","journal-title":"Am. J. Transplant"},{"key":"B15","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s40472-023-00412-6","article-title":"Defiance of occam's razor: recent changes in us kidney allocation and impact on efficiency and marginal kidney outcomes","volume":"10","author":"Jay","year":"2023","journal-title":"Curr. Transplant. Rep"},{"key":"B16","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.ajt.2023.11.015","article-title":"Opportunities and challenges of machine learning in transplant-related studies","volume":"24","author":"Kang","year":"2024","journal-title":"Am. J. Transplant"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0482-1","article-title":"Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network","volume":"18","author":"Katzman","year":"2018","journal-title":"BMC Med. Res. Methodol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"235","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":"B19","doi-asserted-by":"publisher","DOI":"10.1101\/2024.09.11.24313488","article-title":"Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable models","author":"Li","year":"2024","journal-title":"medRxiv"},{"key":"B20","doi-asserted-by":"publisher","first-page":"100570","DOI":"10.1016\/j.xkme.2022.100570","article-title":"Cold ischemia time, kidney donor profile index, and kidney transplant outcomes: a cohort study","volume":"5","author":"Lum","year":"2023","journal-title":"Kidney Med"},{"key":"B21","unstructured":"A unified approach to interpreting model predictions\n          \n          4765\n          4774\n          \n            \n              Lundberg\n              S. M.\n            \n            \n              Lee\n              S.-I.\n            \n          \n          Adv. Neural Inf. Process. Syst\n          30\n          2017"},{"key":"B22","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1111\/ajt.15396","article-title":"Accelerating kidney allocation: simultaneously expiring offers","volume":"19","author":"Mankowski","year":"2019","journal-title":"Am. J. Transplant"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1097\/TP.0000000000001238","article-title":"Predictors of deceased donor kidney discard in the United States","volume":"101","author":"Marrero","year":"2017","journal-title":"Transplantation"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1111\/j.1600-6143.2010.03163.x","article-title":"Improving distribution efficiency of hard-to-place deceased donor kidneys: predicting probability of discard or delay","volume":"10","author":"Massie","year":"2010","journal-title":"Am. J. Transplant"},{"key":"B25","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/transplantology5020006","article-title":"Wasted potential: decoding the trifecta of donor kidney shortage, underutilization, and rising discard rates","volume":"5","author":"McKenney","year":"2024","journal-title":"Transplantology"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e14054","DOI":"10.1111\/ctr.14054","article-title":"Physician and patient acceptance of policies to reduce kidney discard","volume":"34","author":"Mehrotra","year":"2020","journal-title":"Clin. Transplant"},{"key":"B27","doi-asserted-by":"publisher","first-page":"102412","DOI":"10.1016\/j.inffus.2024.102412","article-title":"Designing interpretable ml system to enhance trust in healthcare: a systematic review to proposed responsible clinician-ai-collaboration framework","volume":"108","author":"Nasarian","year":"2024","journal-title":"Inf. Fusion"},{"key":"B28","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1177\/1359105318763510","article-title":"Decision fatigue: a conceptual analysis","volume":"25","author":"Pignatiello","year":"2020","journal-title":"J. Health Psychol"},{"key":"B29","doi-asserted-by":"crossref","unstructured":"scikit-survival: a library for time-to-event analysis built on top of scikit-learn\n          \n          1\n          6\n          \n            \n              P\u00f6lsterl\n              S.\n            \n          \n          J. Mach. Learn. Res\n          21\n          2020","DOI":"10.1007\/978-1-4842-5373-1_1"},{"key":"B30","doi-asserted-by":"publisher","first-page":"zrad011","DOI":"10.1093\/bjsopen\/zrad011","article-title":"Machine learning models in predicting graft survival in kidney transplantation: meta-analysis","volume":"7","author":"Ravindhran","year":"2023","journal-title":"BJS Open"},{"key":"B31","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1111\/ajt.17144","article-title":"Single-center analysis of organ offers and workload for liver and kidney allocation","volume":"22","author":"Reddy","year":"2022","journal-title":"Am. J. Transplant"},{"key":"B32","doi-asserted-by":"publisher","first-page":"10397","DOI":"10.3389\/ti.2022.10397","article-title":"Using information available at the time of donor offer to predict kidney transplant survival outcomes: a systematic review of prediction models","volume":"35","author":"Riley","year":"2022","journal-title":"Transplant. Int"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1725","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":"B34","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1016\/j.ajt.2025.03.010","article-title":"Decreasing efficiency in deceased donor kidney offer notifications under the new distance-based kidney allocation system","volume":"25","author":"Yu","year":"2025","journal-title":"Am. J. Transplant"},{"key":"B35","doi-asserted-by":"publisher","first-page":"16367","DOI":"10.1038\/s41598-023-41162-w","article-title":"simKAP: simulation framework for the kidney allocation process with decision making model","volume":"13","author":"Zhang","year":"2023","journal-title":"Sci. Rep"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1662960\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T15:04:08Z","timestamp":1758207848000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1662960\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"references-count":35,"alternative-id":["10.3389\/frai.2025.1662960"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1662960","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]},"article-number":"1662960"}}