Abstract
Background:
The existing risk prediction models for COVID-19 associated mortality have not considered the difference in risk factors in patients across an aging population.
Aim:
To develop age-specific prediction models to forecast the risk of all-cause mortality in patients recovering from COVID-19 infection
Design:
Population-based, retrospective cohort study
Methods:
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 (<45, 45-64, ≥65) and followed-up for one year. 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.
Results:
Of the 891,246 patients with COVID-19 identified, 13,578 (1.05%) died within one year 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 ≥65 exhibited excellent prediction performance with an AUROC of 0.87 (95% CI: 0.87, 0.88), followed by the model for patients aged 45-64 [AUROC=0.83 (95% CI: 0.81, 0.85)] and those aged <45 [AUROC=0.79 (95% CI: 0.72, 0.86)].
Conclusion:
The age-specific models reported accurately predicted the risk of post-acute mortality in their corresponding age-group of patients, providing valuable asset in optimising clinical strategies and resource allocation in the management of the global burden of Long COVID.
The existing risk prediction models for COVID-19 associated mortality have not considered the difference in risk factors in patients across an aging population.
Aim:
To develop age-specific prediction models to forecast the risk of all-cause mortality in patients recovering from COVID-19 infection
Design:
Population-based, retrospective cohort study
Methods:
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 (<45, 45-64, ≥65) and followed-up for one year. 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.
Results:
Of the 891,246 patients with COVID-19 identified, 13,578 (1.05%) died within one year 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 ≥65 exhibited excellent prediction performance with an AUROC of 0.87 (95% CI: 0.87, 0.88), followed by the model for patients aged 45-64 [AUROC=0.83 (95% CI: 0.81, 0.85)] and those aged <45 [AUROC=0.79 (95% CI: 0.72, 0.86)].
Conclusion:
The age-specific models reported accurately predicted the risk of post-acute mortality in their corresponding age-group of patients, providing valuable asset in optimising clinical strategies and resource allocation in the management of the global burden of Long COVID.
| Original language | English |
|---|---|
| Journal | QJM: An International Journal of Medicine |
| Early online date | 22 Sept 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 22 Sept 2025 |
Bibliographical note
Copyright © The Author(s) 2025. Published by Oxford University Press on behalf of the Association of Physicians. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [email protected].Data Access Statement
The data contains confidential information and hence cannot be shared with the public due to third-party use restrictions. The codes used to derive the current findings are made available in https://github.com/Jiayiz2222/LongCovid_prediction to ensure transparency and reproducibility of the findings reported.Funding
The authors thank the Hospital Authority for the generous provision of data for this study. This work was supported by HMRF Research on COVID-19, The Hong Kong Special Administrative Region (HKSAR) Government (Principal Investigator: EWYC; Ref No. COVID1903011); Collaborative Research Fund, University Grants Committee, the HKSAR Government (Principal Investigator: ICKW; Ref. No. C7154-20GF); and Research Grant from the Health Bureau, the HKSAR Government (Principal Investigator: ICKW; Ref. No. COVID19F01).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- COVID-19
- SARS-CoV-2 infection
- Post-acute sequelae of SARS-CoV-2
- Prediction modelling
- Machine-learning
- All-cause mortality
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