{"doi":"10.1111/ctr.70268","title":"Machine Learning for 1‐Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry","abstract":"BACKGROUND: In regions with limited donor availability, optimizing efficiency in lung transplant decision-making is crucial. Preoperative prediction of 1-year graft failure can enhance candidate selection and clinical decision-making. METHODS: We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning-based model for predicting 1-year graft failure after lung transplantation. A total of 240 cases were analyzed using 5-fold cross-validation. Among 25 preoperative factors associated with 1-year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development. RESULTS: Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733. CONCLUSIONS: Our machine learning-based model effectively predicts 1-year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability.","journal":"Clinical Transplantation","year":2025,"id":545912,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9411,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":80556,"name":"Sunyoung Kwon","orcid":"0000-0003-3433-1409","position":1,"is_corresponding":false},{"id":1437129,"name":"Woo Hyun Cho","orcid":"0000-0002-8299-8008","position":2,"is_corresponding":false},{"id":1040562,"name":"Jin Gu Lee","orcid":"0000-0003-2767-6505","position":3,"is_corresponding":false},{"id":1437130,"name":"Song Yee Kim","orcid":"0000-0001-8627-486X","position":4,"is_corresponding":false},{"id":1437131,"name":"Samina Park","orcid":"0000-0001-9625-2672","position":5,"is_corresponding":false},{"id":1437132,"name":"Kyeongman Jeon","orcid":"0000-0002-4822-1772","position":6,"is_corresponding":false},{"id":1437133,"name":"Hye Ju Yeo","orcid":"0000-0002-8403-5790","position":7,"is_corresponding":false},{"id":1437621,"name":"KOTRY Study Group","orcid":null,"position":8,"is_corresponding":false},{"id":1437128,"name":"Dasom Noh","orcid":"0009-0008-8021-2295","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":null,"created_at":"2026-07-19T02:53:27.751050Z","pmid":"40782091","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}