{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T23:48:43Z","timestamp":1774741723369,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Health and Medical Research Fund Research"},{"name":"The Hong Kong Special Administrative Region"},{"DOI":"10.13039\/501100001839","name":"University Grants Committee","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001839","id-type":"DOI","asserted-by":"publisher"}]},{"name":"HKSAR Government","award":["C7154-20GF"],"award-info":[{"award-number":["C7154-20GF"]}]},{"name":"Research Grant from the Health Bureau"},{"name":"HKSAR Government","award":["COVID19F01"],"award-info":[{"award-number":["COVID19F01"]}]},{"name":"Laboratory of Data Discovery for Health"},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The existing risk prediction models for COVID-19 associated mortality have not considered the differences in risk factors across different age groups of patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To develop age-specific prediction models to forecast the risk of all-cause mortality in patients recovering from COVID-19 infection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Population-based, retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients with COVID-19 between 1 April 2020 and 31 July 2022 survived beyond the acute phase of infection were stratified into separate age cohorts (&amp;lt;45, 45\u201364, \u226565) and followed-up for 1\u2009year. Backward stepwise logistic regression and four statistical and machine learning algorithms were employed to develop age-specific models on the risk of post-acute mortality following COVID-19 infection, based on a comprehensive set of clinical parameters including demographics, COVID-19 vaccination status, pre-existing comorbidities and laboratory-test findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 891\u00a0246 patients with COVID-19 identified, 13\u00a0578 (1.05%) died within 1\u2009year of the index date. Age, COVID-19 vaccination status and history of acute respiratory syndrome prior infection were identified as predictors in the models for separate age groups. The model for patients aged \u226565 exhibited excellent prediction performance with an AUROC of 0.87 (95% CI: 0.87, 0.88), followed by the model for patients aged 45\u201364 [AUROC\u2009=\u20090.83 (95% CI: 0.81, 0.85)] and those aged &amp;lt;45 [AUROC\u2009=\u20090.79 (95% CI: 0.72, 0.86)].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The age-specific models accurately predicted the risk of post-acute mortality in their corresponding age groups of patients, providing valuable asset in optimizing clinical strategies and resource allocation in the management of the global burden of Long COVID.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/qjmed\/hcaf218","type":"journal-article","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T11:53:46Z","timestamp":1758369226000},"page":"117-128","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of age-specific predictive models on the risk of post-acute mortality within 1\u2009year of COVID-19 infection"],"prefix":"10.1093","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5891-3940","authenticated-orcid":false,"given":"Ivan Chun Hang","family":"Lam","sequence":"first","affiliation":[{"name":"Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR,","place":["China"]},{"name":"Pharmaco- and Device Epidemiology, Centre for Statistics in Medicines, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford , Oxford,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong 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