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. 2023 Apr 19;52(2):355-376.
doi: 10.1093/ije/dyad012.

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Christiana Kartsonaki  1 , J Kenneth Baillie  2   3 , Noelia García Barrio  4 , Joaquín Baruch  5 , Abigail Beane  6 , Lucille Blumberg  7 , Fernando Bozza  8 , Tessa Broadley  9 , Aidan Burrell  9 , Gail Carson  5 , Barbara Wanjiru Citarella  5 , Andrew Dagens  5 , Emmanuelle A Dankwa  10 , Christl A Donnelly  10   11 , Jake Dunning  5 , Loubna Elotmani  12 , Martina Escher  5 , Nataly Farshait  13 , Jean-Christophe Goffard  14 , Bronner P Gonçalves  5 , Matthew Hall  15 , Madiha Hashmi  16 , Benedict Sim Lim Heng  17 , Antonia Ho  18 , Waasila Jassat  7 , Miguel Pedrera Jiménez  4 , Cedric Laouenan  19 , Samantha Lissauer  20 , Ignacio Martin-Loeches  21 , France Mentré  19 , Laura Merson  5   22 , Ben Morton  23 , Daniel Munblit  24   25 , Nikita A Nekliudov  26 , Alistair D Nichol  27 , Budha Charan Singh Oinam  28 , David Ong  29 , Prasan Kumar Panda  28 , Michele Petrovic  13 , Mark G Pritchard  5 , Nagarajan Ramakrishnan  30 , Grazielle Viana Ramos  8 , Claire Roger  12 , Oana Sandulescu  31   32 , Malcolm G Semple  33   34 , Pratima Sharma  35 , Louise Sigfrid  5 , Emily C Somers  35 , Anca Streinu-Cercel  31 , Fabio Taccone  14 , Pavan Kumar Vecham  30 , Bharath Kumar Tirupakuzhi Vijayaraghavan  36   37 , Jia Wei  15 , Evert-Jan Wils  29 , Xin Ci Wong  38 , Peter Horby  5 , Amanda Rojek  5   39   40 , Piero L Olliaro  5 ; ISARIC Clinical Characterisation Group
Collaborators, Affiliations

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Christiana Kartsonaki et al. Int J Epidemiol. .

Abstract

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients.

Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV).

Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%.

Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

Keywords: COVID-19; SARS-CoV-2; co-morbidities; cohort study; risk of death; symptoms; treatments.

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Conflict of interest statement

Donnelly, C.A. declares research funding from the UK Medical Research Council and the UK National Institute for Health Research. Ho, A. declares grant funding from Medical Research Council UK, Scottish Funding Council—Grand Challenges Research Fund, and the Wellcome Trust, outside this submitted work. Martin-Loeches I. declared lectures for Gilead, Thermofisher, Pfizer, MSD; advisory board participation for Fresenius Kabi, Advanz Pharma, Gilead, Accelerate, Merck; and consulting fees for Gilead outside of the submitted work. Mentré F, declares consulting fees from IPSEN, Servier and Da Volterra, and reports research grants to her group from Sanofi, Roche, Servier and Da Voleterra, all outside the submitted work. Nichol, A. declares a grant from the Health Research Board of Ireland to support data collection in Ireland (CTN-2014–012), an unrestricted grant from BAXTER for the TAME trial kidney substudy and consultancy fees paid to his institution from AM-PHARMA. Semple, M.G. reports grants from DHSC National Institute of Health Research UK, from the Medical Research Council UK and from the Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, supporting the conduct of the study; other interest in Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. Streinu-Cercel, Anca has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, Celltrion, Inc. and Atriva Therapeutics, outside the scope of the submitted work.

Figures

Figure 1
Figure 1
Numbers of participants. 6321 (0.92%) of the participants included in the main analysis were admitted to hospital for isolation
Figure 2
Figure 2
Numbers of patients by country
Figure 3
Figure 3
Symptom prevalence by age (n = 290 750)
Figure 4
Figure 4
Proportions meeting each symptom definition by age (n = 290 750). CDC, Centers for Disease Control and Prevention; ECDC, European Centre for Disease Prevention and Control; PHE, Public Health England; WHO, World Health Organization
Figure 5
Figure 5
Prevalence of pre-existing co-morbidities and risk factors (n = 689 572)
Figure 6
Figure 6
Proportion who have received each treatment (n = 290 750)
Figure 7
Figure 7
Case-fatality ratio by country. Each point is a country and points are coloured and have different shapes by region. The horizontal line is the inverse-variance weighted average case-fatality ratio. The funnel plot shows the 95% confidence limits. The x-axis is on a log10 scale
Figure 8
Figure 8
Cumulative incidence curves of death and discharge by sex (n = 689 572)
Figure 9
Figure 9
Hazard ratios and 95% confidence intervals for death by age group and sex (n = 689 572). The model is stratified by country. The reference group is females of age [20,30). The y-axis is plotted on a logarithmic scale
Figure 10
Figure 10
Associations of (A) co-morbidities (n = 689 572) and (B) symptoms (n = 290 750) with risk of death. Dots are hazard ratios and lines are 95% confidence intervals of death by each variable at a time (the reference group is not having the particular symptom/comorbidity/risk factor). Models were adjusted for age and age2, stratified by sex and country
Figure 11
Figure 11
Hazard ratios and 95% confidence intervals for (A) admission to an intensive care unit and (B) use of invasive mechanical ventilation by age (n = 689 572)
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