QJM : monthly journal of the Association of Physicians · 2026
Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Lam Ivan Chun Hang, Zhou Jiayi, Liu Wenlong, Man Kenneth Keng Cheung, Zhang Qingpeng, Luo Hao, Wong Carlos King Ho, Chui Celine Sze Ling, Lai Francisco Tsz Tsun, Li Xue, Chan Esther Wai Yin, Wong Ian Chi Kei, Wan Eric Yuk Fai
Аннотация
BACKGROUND: The existing risk prediction models for COVID-19 associated mortality have not considered the differences in risk factors across different age groups of patients.
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 1 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 1 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 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.
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 1 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 1 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 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.