Characteristics and outcomes of an international cohort of 400,000 hospitalised patients with Covid-19
Authors:   ISARIC Clinical Characterisation Group, Kartsonaki Christiana 
Published: September 2021

Background

Policymakers need robust data to respond to the COVID-19 pandemic. We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, the world’s largest international, standardised cohort of hospitalised patients.

Methods

The dataset analysed includes COVID-19 patients hospitalised between January 2020 and May 2021. We investigated how symptoms on admission, comorbidities, risk factors, and treatments varied by age, sex, and other characteristics. We used Cox proportional hazards models to investigate associations between demographics, symptoms, comorbidities, and other factors with risk of death, admission to intensive care unit (ICU), and invasive mechanical ventilation (IMV).

Findings

439,922 patients with laboratory-confirmed (91.7%) or clinically-diagnosed (8.3%) SARS-CoV-2 infection from 49 countries were enrolled. Age (adjusted hazard ratio [HR] per 10 years 1.49 [95% CI 1.49-1.50]) and male sex (1.26 [1.24-1.28]) were associated with a higher risk of death. Rates of admission to ICU and use of IMV increased with age up to age 60, then dropped. Symptoms, comorbidities, and treatments varied by age and had varied associations with clinical outcomes. Tuberculosis was associated with an 86% higher risk of death, and HIV with an 87% higher risk of death. Case fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients.

Interpretation

The size of our international database and the standardized data collection method makes this study a reliable and comprehensive international description of COVID-19 clinical features. This is a viable model to be applied to future epidemics.

Funding

UK Foreign, Commonwealth and Development Office, the Bill & Melinda Gates Foundation and Wellcome. See acknowledgements section for funders of sites that contributed data.

Research in context

Evidence before this study

To identify large, international analyses of hospitalised COVID-19 patients that used standardised data collection, we conducted a systematic review of the literature from 1 Jan 2020 to 28 Apr 2020. We identified 78 studies, with data from 77,443 people (1) predominantly from China. We could not find any studies including data from low and middle-income countries. We repeated our search on 18 Aug 2021 but could not identify any further studies that met our inclusion criteria.

Added value of this study

Our study uses standardised clinical data collection to collect data from a vast number of patients across the world, including patients from low-, middle-, and high-income countries. The size of our database gives us great confidence in the accuracy of our descriptions of the global impact of COVID-19. We can confirm findings reported by smaller, country-specific studies and compare clinical data between countries. We have demonstrated that it is possible to collect large volumes of standardised clinical data during a pandemic of a novel acute respiratory infection. The results provide a valuable resource for present policymakers and future global health researchers.

Implications of all the available evidence

Presenting symptoms of SARS-CoV-2 infection in patients requiring hospitalisation are now well-described globally, with the most common being fever, cough, and shortness of breath. Other symptoms also commonly occur, including altered consciousness in older adults and gastrointestinal symptoms in younger patients, and age can influence the likelihood of a patient having symptoms that match one or more case definitions. There are geographic and temporal variations in the case fatality rate (CFR), but overall, CFR was 20.6% in this large international cohort of hospitalised patients with a median age of 60 years (IQR: 45 to 74 years).