Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection

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.

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Publication Details

Journal
QJM
Published
2025-09-20
DOI
https://doi.org/10.1093/qjmed/hcaf218
Primary Topic
COVID-19 Clinical Research Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection

Celine Sze Ling Chui, Hao Luo, Wenlong Liu, Eric Yuk Fai Wan et al.
QJM
COVID-19 Clinical Research Studies
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Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection

Celine Sze Ling Chui, Hao Luo, Wenlong Liu, Eric Yuk Fai Wan, Esther W. Chan, Kenneth K. C. Man, Ivan Chun Hang Lam, Carlos King Ho Wong, Francisco Tsz Tsun Lai, Qingpeng Zhang, Ian Chi Kei Wong, Xue Li, Jiayi Zhou
article en

Abstract

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.

QJMVol. 119(2)
Macau University of Science and Technology (MO), University of London (GB), University College London Hospitals NHS Foundation Trust (GB), University of Waterloo (CA), Aston University (GB), Chinese University of Hong Kong (HK), Hong Kong Science and Technology Parks Corporation (HK), Nuffield Orthopaedic Centre (GB), Education University of Hong Kong (HK), Hong Kong Jockey Club (HK), City University of Hong Kong, Shenzhen Research Institute (CN), Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT), Institute for Safe Medication Practices (US), University of Hong Kong - Shenzhen Hospital (CN), Institut de Recherche et d’Innovation (FR), Laboratory of Data Discovery for Health (HK), University College London (GB), University of Hong Kong (HK)
Openalex Percentile: Top 69%
COVID-19 Clinical Research Studies
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