{"doi":"10.1111/ctr.70325","title":"Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant","abstract":"BACKGROUND: Transplant is the optimal treatment for kidney failure; however, disparities in access persist. We developed and validated risk indices to predict early dropout at key stages of the transplant-seeking process not captured in national registries. METHODS: We included patients referred for kidney transplant at Houston Methodist Hospital between June 2016, and November 2023. We collected demographic, clinical, patient- and contextual-level socioeconomic variables from electronic health records and publicly available census data. We used machine learning (ML) models to predict the characteristics of patients at higher risk of dropping out: (1) at referral (before starting evaluation), (2) in the process of evaluation (before waitlisting), and (3) during waitlisting (before receiving a transplant). Model performance was evaluated using AUROC. RESULTS: Of 4133 referred patients, 46% did not attend their first transplant evaluation visit. Of 2414 patients who were medically eligible for transplant and started evaluation, 54% did not become waitlisted. Of 2457 waitlisted patients, 31% became inactive on the waitlist. Higher risk patients were consistently older, obese, and socioeconomically disadvantaged, with stage-specific differences: social factors-such as being single, unemployed, less educated, and living in high-deprivation areas-and African American race dominated at referral (AUROC 0.79); clinical comorbidities and both African American and Hispanic ethnicity were prominent at evaluation (AUROC 0.71); and Hispanic ethnicity, smoking, and digital exclusion were key drivers at waitlisting (AUROC 0.76). CONCLUSION: ML models effectively identified dropout risk at referral, evaluation, and waitlisting, enabling early identification of at-risk patients. Targeted interventions could reduce disparities, improve evaluation completion, and increase transplant access.","journal":"Clinical Transplantation","year":2025,"id":575311,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9168,"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":1049589,"name":"Enshuo Hsu","orcid":"0000-0002-8137-0171","position":1,"is_corresponding":false},{"id":702139,"name":"Thomas Potter","orcid":"0000-0001-7884-4172","position":2,"is_corresponding":false},{"id":527046,"name":"Ioannis A. Kakadiaris","orcid":"0000-0002-0591-1079","position":3,"is_corresponding":false},{"id":320242,"name":"David A. Axelrod","orcid":"0000-0001-5684-0613","position":4,"is_corresponding":false},{"id":1483615,"name":"Faith Parsons","orcid":null,"position":5,"is_corresponding":false},{"id":1483246,"name":"Andrea M. Meinders","orcid":"0000-0001-6873-5087","position":6,"is_corresponding":false},{"id":1275770,"name":"Victoria Cassell","orcid":"0000-0002-2813-230X","position":7,"is_corresponding":false},{"id":1483616,"name":"Catherine Pulicken","orcid":null,"position":8,"is_corresponding":false},{"id":1414326,"name":"Zulqarnain Javed","orcid":"0000-0002-4137-9198","position":9,"is_corresponding":false},{"id":282098,"name":"Paula K. Shireman","orcid":"0000-0002-9701-5422","position":10,"is_corresponding":false},{"id":317156,"name":"Stefano Casarin","orcid":"0000-0002-7416-2366","position":11,"is_corresponding":false},{"id":1483617,"name":"Allison Gelfond","orcid":null,"position":12,"is_corresponding":false},{"id":419716,"name":"Amy D. Waterman","orcid":"0000-0002-7799-0060","position":13,"is_corresponding":false},{"id":56889,"name":"Solaf Al Awadhi","orcid":null,"position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-19T02:57:48.486077Z","pmid":"40971151","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":[]}