{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T19:52:59Z","timestamp":1793303579420,"version":"4.5.0"},"reference-count":20,"publisher":"American Medical Association (AMA)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Thirty-day hospital readmission is used to guide postdischarge care; however, a single binary 30-day estimate does not distinguish when risk occurs from when intervention is most actionable, with implications for allocation of postdischarge resources.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate time-structured hospital readmission risk within 30 days after discharge and implications for clinical utility.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n Retrospective prognostic study of adults with hospital discharges from 2017 to 2024 (from 19 hospitals within a US health system for the development cohort) and 2008 to 2019 (from Medical Information Mart for Intensive Care IV for the external cohort) with 30-day follow-up. Analyses were conducted from November 2025 through June 2026.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n Postdischarge prediction window, including landmark intervals of 0 to 7, 8 to 14, 15 to 21, and 22 to 30 days and cumulative horizons through days 7, 14, 21, and 30 after discharge.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n First all-cause hospital readmission before death during each landmark interval and by each cumulative horizon. Risk estimates were derived using clinical information available at hospital discharge. Performance was assessed using discrimination, calibration, decision curves, and capacity-constrained analyses.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among 617\u202f841 discharges in the development cohort, the median (IQR) patient age was 65 (50-76) years, and 315\u202f220 discharges (51.0%) occurred among female patients. The 30-day readmission rate was 16.3% (95% CI, 16.2%-16.5%), with 68\u202f797 readmissions (68.2%) occurring within 14 days. Cumulative-horizon analyses characterized readmission risk through days 7, 14, 21, and 30, whereas landmark interval-specific analyses showed lower positive predictive values (PPVs) beyond day 7. At 5% intervention capacity, landmark PPVs were 58.3% (95% CI, 57.4%-59.1%) for days 0 to 7, 16.0% (95% CI, 15.5%-16.6%) for days 8 to 14, 14.5% (95% CI, 13.9%-15.0%) for days 15 to 21, and 12.9% (95% CI, 12.4% to 13.5%) for days 22 to 30; numbers needed to prevent 1 readmission were 8.6, 31.3, 34.6, and 38.6, respectively.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this prognostic study of adult hospital discharges, hospital readmission risk varied over time and was not captured by a single binary 30-day estimate. These findings suggest that time-structured readmission modeling can characterize when readmission risk occurs, but additional work is needed before such models can reliably guide interventions during specific postdischarge intervals.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.28846","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T15:30:28Z","timestamp":1786635028000},"page":"e2628846","source":"Crossref","is-referenced-by-count":1,"title":["Timing of 30-Day Hospital Readmission Risk and Its Implications for Clinical Utility"],"prefix":"10.1001","volume":"9","author":[{"given":"Mohamad Zafer","family":"Alkayali","sequence":"first","affiliation":[{"name":"Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuelei","family":"Fu","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janna C.","family":"Castro","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matt","family":"Gill","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shant","family":"Ayanian","sequence":"additional","affiliation":[{"name":"Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar B.","family":"Dugani","sequence":"additional","affiliation":[{"name":"Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota"},{"name":"Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"issue":"1","key":"zoi260784r2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12874-021-01284-z","article-title":"Application of machine learning in predicting hospital readmissions: a scoping review of the literature.","volume":"21","author":"Huang","year":"2021","journal-title":"BMC Med Res Methodol"},{"issue":"15","key":"zoi260784r3","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1001\/jama.2011.1515","article-title":"Risk prediction models for hospital readmission: a systematic review.","volume":"306","author":"Kansagara","year":"2011","journal-title":"JAMA"},{"issue":"4","key":"zoi260784r4","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1001\/jama.2012.216476","article-title":"Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia.","volume":"309","author":"Dharmarajan","year":"2013","journal-title":"JAMA"},{"key":"zoi260784r5","doi-asserted-by":"publisher","first-page":"h411","DOI":"10.1136\/bmj.h411","article-title":"Trajectories of risk after hospitalization for heart failure, acute myocardial infarction, or pneumonia: retrospective cohort study.","volume":"350","author":"Dharmarajan","year":"2015","journal-title":"BMJ"},{"issue":"7","key":"zoi260784r6","doi-asserted-by":"publisher","first-page":"E391","DOI":"10.1503\/cmaj.101860","article-title":"Proportion of hospital readmissions deemed avoidable: a systematic review.","volume":"183","author":"van Walraven","year":"2011","journal-title":"CMAJ"},{"issue":"4","key":"zoi260784r7","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1001\/jamainternmed.2015.7863","article-title":"Preventability and causes of readmissions in a national cohort of general medicine patients.","volume":"176","author":"Auerbach","year":"2016","journal-title":"JAMA Intern Med"},{"issue":"1","key":"zoi260784r8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset.","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"zoi260784r10","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"issue":"6","key":"zoi260784r11","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1161\/CIRCULATIONAHA.115.017719","article-title":"Introduction to the analysis of survival data in the presence of competing risks.","volume":"133","author":"Austin","year":"2016","journal-title":"Circulation"},{"issue":"6","key":"zoi260784r13","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models.","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"issue":"21","key":"zoi260784r14","doi-asserted-by":"publisher","first-page":"2534","DOI":"10.1200\/JCO.2015.65.5654","article-title":"Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use.","volume":"34","author":"Kerr","year":"2016","journal-title":"J Clin Oncol"},{"issue":"11","key":"zoi260784r15","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.41272","article-title":"Outpatient follow-up and 30-day readmissions: a systematic review and meta-analysis.","volume":"8","author":"Balasubramanian","year":"2025","journal-title":"JAMA Netw Open"},{"issue":"7028","key":"zoi260784r16","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1136\/bmj.312.7028.426","article-title":"The number needed to treat: a clinically useful nomogram in its proper context.","volume":"312","author":"Chatellier","year":"1996","journal-title":"BMJ"},{"issue":"6","key":"zoi260784r17","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1503\/cmaj.091117","article-title":"Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community.","volume":"182","author":"van Walraven","year":"2010","journal-title":"CMAJ"},{"issue":"4","key":"zoi260784r18","doi-asserted-by":"publisher","first-page":"545","DOI":"10.3390\/jpm12040545","article-title":"LACE index to predict the high risk of 30-day readmission: a systematic review and meta-analysis.","volume":"12","author":"Rajaguru","year":"2022","journal-title":"J Pers Med"},{"issue":"8","key":"zoi260784r19","doi-asserted-by":"publisher","first-page":"520","DOI":"10.7326\/0003-4819-155-8-201110180-00008","article-title":"Interventions to reduce 30-day rehospitalization: a systematic review.","volume":"155","author":"Hansen","year":"2011","journal-title":"Ann Intern Med"},{"issue":"11","key":"zoi260784r20","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.44825","article-title":"Transitional care interventions from hospital to community to reduce health care use and improve patient outcomes: a systematic review and network meta-analysis.","volume":"6","author":"Tyler","year":"2023","journal-title":"JAMA Netw Open"},{"key":"zoi260784r1","unstructured":"Agency for Healthcare Research and Quality. Introduction to the HCUP Nationwide Readmissions Database (NRD), 2022. 2024. Accessed March 4, 2026. https:\/\/hcup-us.ahrq.gov\/db\/nation\/nrd\/NRDIntroduction2022.pdf"},{"key":"zoi260784r9","unstructured":"Johnson? A, Bulgarelli? L, Pollard? T, . Data from: MIMIC-IV.? 2024;Version 3.1. Accessed July 7, 2026. https:\/\/physionet.org\/content\/mimiciv\/3.1\/"},{"key":"zoi260784r12","doi-asserted-by":"crossref","unstructured":"Chen? T, Guestrin? C. XGBoost: a scalable tree boosting system. arXiv. Preprint posted March 9, 2016. doi:10.48550\/arXiv.1603.02754","DOI":"10.1145\/2939672.2939785"}],"container-title":["JAMA Network Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/articlepdf\/2852734\/alkayali_2026_oi_260784_1785962763.02332.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T15:30:30Z","timestamp":1786635030000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2852734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":20,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,3]]}},"URL":"https:\/\/doi.org\/10.1001\/jamanetworkopen.2026.28846","relation":{},"ISSN":["2574-3805"],"issn-type":[{"value":"2574-3805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}