{"doi":"10.3389/fimmu.2023.1194338","title":"Maximizing utility of nondirected living liver donor grafts using machine learning","abstract":"Objective: There is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD). Materials and method: Using OPTN living donor liver transplant (LDLT) data (1/1/2000-12/31/2019), we identified 6328 LDLTs (4621 right, 644 left, 1063 left-lateral grafts). Random forest survival models were constructed to predict 10-year graft survival for each of the 3 graft types. Results: Donor-to-recipient body surface area ratio was an important predictor in all 3 models. Other predictors in all 3 models were: malignant diagnosis, medical location at LDLT (inpatient/ICU), and moderate ascites. Biliary atresia was important in left and left-lateral graft models. Re-transplant was important in right graft models. C-index for 10-year graft survival predictions for the 3 models were: 0.70 (left-lateral); 0.63 (left); 0.61 (right). Similar C-indices were found for 1-, 3-, and 5-year graft survivals. Comparison of model predictions to actual 10-year graft survivals demonstrated that the predicted upper quartile survival group in each model had significantly better actual 10-year graft survival compared to the lower quartiles (p<0.005). Conclusion: When applied in clinical context, our models assist with the identification and stratification of potential recipients for hepatic grafts from ND-LLD based on predicted graft survivals, while accounting for complex donor-recipient interactions. These analyses highlight the unmet need for granular data collection and machine learning modeling to identify potential recipients who have the best predicted transplant outcomes with ND-LLD grafts.","journal":"Frontiers in Immunology","year":2023,"id":354948,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6569,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":339257,"name":"Nicole J. Kim","orcid":"0000-0003-2348-0580","position":1,"is_corresponding":false},{"id":1102452,"name":"Mark Sturdevant","orcid":"0000-0002-6189-1447","position":2,"is_corresponding":false},{"id":1078659,"name":"James D. Perkins","orcid":"0000-0002-6935-0012","position":3,"is_corresponding":false},{"id":1081945,"name":"Catherine E. Kling","orcid":"0000-0002-3763-8214","position":4,"is_corresponding":false},{"id":570358,"name":"Ramasamy Bakthavatsalam","orcid":"0000-0002-5507-9719","position":5,"is_corresponding":false},{"id":1102453,"name":"Patrick J. Healey","orcid":"0000-0002-8977-0815","position":6,"is_corresponding":false},{"id":1078660,"name":"André A. S. Dick","orcid":"0000-0002-8052-7632","position":7,"is_corresponding":false},{"id":1074432,"name":"Jorgé Reyes","orcid":"0000-0003-3065-8674","position":8,"is_corresponding":false},{"id":621918,"name":"Scott W. Biggins","orcid":"0000-0002-3081-4668","position":9,"is_corresponding":false},{"id":1102451,"name":"Kiran Bambha","orcid":"0000-0002-8223-5246","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T01:13:11.976628Z","pmid":"37457719","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":[]}