Characteristics and outcomes of an international cohort of 400,000 hospitalised patients with Covid-19
Notice bibliographique
Résumé
Abstract 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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».