{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:09:15Z","timestamp":1789049355795,"version":"build-2803163510"},"reference-count":26,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011839","name":"Jefferson Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011839","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Transplantation"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influencing waitlist outcomes. We utilized machine learning (ML) to identify factors associated with waitlist mortality, combining variables associated with institutional offer acceptance practices as well as candidate\u2010specific risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed the Organ Procurement and Transplantation Network database for pediatric HTx candidates listed between 2010 and 2020. Various statistical and ML models were employed to identify predictors of waitlist mortality or clinical deterioration leading to waitlist removal. The dataset was split into training (82%) and testing (18%), and the final model was selected based on predictive performance. SHAP values were used to assess variable importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 5523 pediatric candidates, overall waitlist mortality was 9.8%. The CatBoost model achieved the highest predictive performance with an AUC\u2010ROC score of 0.74 and a recall score of 0.75. Key predictors included candidate diagnosis, age\/size, ventilator use, eGFR, serum albumin, ECMO, and institutional factors such as high offer refusal rates and low transplant volume.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Institutional organ offer acceptance practices influence waitlist outcomes for pediatric HTx candidates. Centers with higher organ refusal rates are associated with worse outcomes, independent of candidate\u2010specific risk factors, underscoring the need for standardizing organ acceptance criteria across institutions to reduce variability in decision\u2010making and improve waitlist survival. Additionally, addressing modifiable risk factors such as malnutrition and renal dysfunction could further optimize patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/petr.70095","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T05:06:12Z","timestamp":1745816772000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0173-5128","authenticated-orcid":false,"given":"Firezer","family":"Haregu","sequence":"first","affiliation":[{"name":"Pediatric Cardiology University of Virginia Children's Hospital  Charlottesville Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-0597","authenticated-orcid":false,"given":"R. Jerome","family":"Dixon","sequence":"additional","affiliation":[{"name":"Data Science University of Virginia  Charlottesville Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4837-6819","authenticated-orcid":false,"given":"Michael","family":"McCulloch","sequence":"additional","affiliation":[{"name":"Pediatric Cardiology University of Virginia Children's Hospital  Charlottesville Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-3578","authenticated-orcid":false,"given":"Michael","family":"Porter","sequence":"additional","affiliation":[{"name":"Data Science University of Virginia  Charlottesville Virginia USA"},{"name":"Systems and Information Engineering University of Virginia  Charlottesville Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2020.02.008"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2014.09.018"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/petr.13671"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajt.2023.02.008"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/tri.12835"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2013.09.018"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/petr.14649"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/petr.14747"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1097\/TP.0000000000003514"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/petr.14525"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2024.05.049"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537\u2010020\u201000369\u20108"},{"key":"e_1_2_10_14_1","doi-asserted-by":"crossref","unstructured":"T.Akiba S.Sano T.Yanase T.Ohta andM.Koyama \u201cOptuna: A Next\u2010Generation Hyperparameter Optimization Framework \u201d Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019):2623\u20132631 https:\/\/doi.org\/10.1145\/3292500.3330701.","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_2_10_15_1","first-page":"4766","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg S. 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