{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T20:30:26Z","timestamp":1786480226551,"version":"build-2736575974"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["R01DK137881"],"award-info":[{"award-number":["R01DK137881"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["R01DK137881"],"award-info":[{"award-number":["R01DK137881"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Kidney360"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Machine learning models predicted short-term AKI reversal and progression with area under the receiver operating characteristic curves of 0.79\u20130.93 across four health systems.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Top predictors common to both outcomes included serum creatinine, systolic BP, and albumin, with consistent patterns across diverse sites.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Early AKI recovery is often transient, with frequent relapse, highlighting the need for continued monitoring after initial improvement.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>AKI affects 10%\u201325% of hospitalized patients and is associated with significant morbidity, mortality, and long-term adverse outcomes. Recovery patterns vary greatly, from full reversal to progression toward CKD. Predicting short-term AKI states and understanding their dynamic trajectories could help optimize clinical interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study used data from four health care systems in the Greater Plains Collaborative, spanning 2009\u20132022. A total of 94,531 inpatient encounters (54% male, 80% White patients) from 75,312 adults with admission eGFR \u226515 ml\/min per 1.73 m\n                      <jats:sup>2<\/jats:sup>\n                      and without prior dialysis or kidney failure were included. We developed CatBoost models, a gradient-boosting decision-tree algorithm, to predict 7-day AKI progression and reversal and then performed multistate modeling to estimate transition intensities and covariate effects on AKI state changes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The models achieved strong performance across outcomes and subgroups: area under the receiver operating characteristic curve 0.79\u20130.89 and accuracy 0.7\u20130.79 for reversal; area under the receiver operating characteristic curve 0.91\u20130.93 and accuracy 0.83\u20130.87 for progression. Serum creatinine, systolic BP (SBP), and albumin were key predictors across both outcomes. Low SBP (\u2264120 mm Hg) predicted increased likelihood of progression, whereas high SBP (\u2265140 mm Hg) predicted asymmetric recovery with reduced chances of subsequent reversal. Multistate analysis highlighted the dynamic and transient nature of AKI recovery, with nearly half of AKI-1 patients (instantaneous hazard rate=0.46) transitioning to \u201cno AKI\u201d peaking on the first day postonset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents a two-stage analytical framework linking short-term prediction with multistate modeling to characterize early recovery and progression patterns after hospital-acquired AKI. The approach highlights a small set of routinely measured variables, such as serum creatinine trajectories and BP trends, that consistently track these dynamics across health systems. While the results are hypothesis-generating and require external, prospective validation, they lay groundwork for future studies on refining risk stratification and exploring targeted interventions in AKI care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.34067\/kid.0000001052","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:03:26Z","timestamp":1764954206000},"page":"279-290","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Predicting and Modeling Recovery Dynamics Post\u2013Acute Kidney Injury"],"prefix":"10.34067","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1154-5098","authenticated-orcid":false,"given":"Qi","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1776-2533","authenticated-orcid":false,"given":"Alan S.L.","family":"Yu","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-6607","authenticated-orcid":false,"given":"Ho Yin","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sravani","family":"Chandaka","sequence":"additional","affiliation":[{"name":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8956-1466","authenticated-orcid":false,"given":"Abu Saleh Mohammad","family":"Mosa","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama"},{"name":"Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri School of Medicine, Columbia, Missouri"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3212-7845","authenticated-orcid":false,"given":"A. 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