{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:42:53Z","timestamp":1784544173524,"version":"3.55.0"},"reference-count":61,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2010seeking process not captured in national registries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We included patients referred for kidney transplant at Houston Methodist Hospital between June 2016, and November 2023. We collected demographic, clinical, patient\u2010 and contextual\u2010level 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2010specific differences: social factors\u2014such as being single, unemployed, less educated, and living in high\u2010deprivation areas\u2014and 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models effectively identified dropout risk at referral, evaluation, and waitlisting, enabling early identification of at\u2010risk patients. Targeted interventions could reduce disparities, improve evaluation completion, and increase transplant access.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ctr.70325","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T14:46:09Z","timestamp":1758293169000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Developing and Validating Machine Learning\u2010Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant"],"prefix":"10.1111","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3420-5859","authenticated-orcid":false,"given":"Solaf","family":"Al Awadhi","sequence":"first","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enshuo","family":"Hsu","sequence":"additional","affiliation":[{"name":"Center for Health Data Science &amp; Analytics, Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas B. H.","family":"Potter","sequence":"additional","affiliation":[{"name":"Center for Health Data Science &amp; Analytics, Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis A.","family":"Kakadiaris","sequence":"additional","affiliation":[{"name":"Department of Computer\u00a0Science University of Houston  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5684-0613","authenticated-orcid":false,"given":"David A.","family":"Axelrod","sequence":"additional","affiliation":[{"name":"Department of Surgery University of Iowa  Iowa City Iowa USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faith","family":"Parsons","sequence":"additional","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6873-5087","authenticated-orcid":false,"given":"Andrea M.","family":"Meinders","sequence":"additional","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria","family":"Cassell","sequence":"additional","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Pulicken","sequence":"additional","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulqarnain","family":"Javed","sequence":"additional","affiliation":[{"name":"Department of Cardiology Center for Cardiovascular Computation and Precision Health, Houston Methodist Research Institute  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula K.","family":"Shireman","sequence":"additional","affiliation":[{"name":"Departments of Medical Physiology and Primary Care &amp; Rural Medicine Texas A&amp;M College of Medicine  Bryan Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Casarin","sequence":"additional","affiliation":[{"name":"Department of Surgery Houston Methodist  Houston Texas USA"},{"name":"Center for Precision Surgery, Houston Methodist Research Institute  Houston Texas USA"},{"name":"LaSIE, UMR 7356 CNRS, La Rochelle Universit\u00e9  La Rochelle France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. 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