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Enregistrement W3082505470 · doi:10.1101/2020.08.27.20183434

Risk factors for severe outcomes of COVID-19: a rapid review

2020· review· en· W3082505470 sur OpenAlexaffabout
Aireen Wingert, Jennifer Pillay, Michelle Gates, Samantha Guitard, Sholeh Rahman, Andrew Beck, Ben Vandermeer, Lisa Hartling

Notice bibliographique

RevuemedRxiv · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueCOVID-19 Clinical Research Studies
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineConfoundingMEDLINECoronavirus disease 2019 (COVID-19)Data extractionGerontologyFamily medicineDemographyDiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Background Identification of high-risk groups is needed to inform COVID-19 vaccine prioritization strategies in Canada. A rapid review was conducted to determine the magnitude of association between potential risk factors and risk of severe outcomes of COVID-19. Methods Methods, inclusion criteria, and outcomes were prespecified in a protocol that is publicly available. Ovid MEDLINE(R) ALL, Epistemonikos COVID-19 in L·OVE Platform, and McMaster COVID-19 Evidence Alerts, and select websites were searched to 15 June 2020. Studies needed to be conducted in Organisation for Economic Co-operation and Development countries and have used multivariate analyses to adjust for potential confounders. After piloting, screening, data extraction, and quality appraisal were all performed by a single reviewer. Authors collaborated to synthesize the findings narratively and appraise the certainty of the evidence for each risk factor-outcome association. Results Of 3,740 unique records identified, 34 were included in the review. The studies included median 596 (range 44 to 418,794) participants with a mean age between 42 and 84 years. Half of the studies (17/34) were conducted in the United States and 19/34 (56%) were rated as good quality. There was low or moderate certainty evidence for a large (≥2-fold) association with increased risk of hospitalization in people having confirmed COVID-19, for the following risk factors: obesity class III, heart failure, diabetes, chronic kidney disease, dementia, age over 45 years (vs. younger), male gender, Black race/ethnicity (vs. non-Hispanic white), homelessness, and low income (vs. above average). Age over 60 and 70 years may be associated with large increases in the rate of mechanical ventilation and severe disease, respectively. For mortality, a large association with increased risk may exist for liver disease, Bangladeshi ethnicity (vs. British white), age over 45 years (vs. <45 years), age over 80 years (vs. 65-69 years), and male gender in those 20-64 years (but not older). Associations with hospitalization and mortality may be very large (≥5-fold increased risk) for those aged over 60 years. Conclusion Among other factors, increasing age (especially >60 years) appears to be the most important risk factor for severe outcomes among those with COVID-19. There is a need for high quality primary research (accounting for multiple confounders) to better understand the level of risk that might be associated with immigration or refugee status, religion or belief system, social capital, substance use disorders, pregnancy, Indigenous identity, living with a disability, and differing levels of risk among children. PROSPERO registration CRD42020198001 What is already known The novel nature of COVID-19 means that in many countries there are currently no pre-determined priority groups for the receipt of the eventual COVID-19 vaccine(s). Primary research is rapidly emerging, but consensus on who might be at increased risk of severe outcomes from COVID-19 has not been established. What this study adds This rapid review shows that advancing age (>45 years and especially >60 years) may be the most important risk factor for hospitalization and mortality from COVID-19. Other important risk factors for severe disease identified by this review include several pre-existing chronic conditions (class III obesity, heart failure, diabetes, chronic kidney disease, liver disease, dementia), male gender, Black race/ethnicity (vs. non-Hispanic white), Bangladeshi ethnicity (vs. British white), low income (vs. high), and homelessness.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,452
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,947
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,452
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0080,003
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,220
Tête enseignante GPT0,513
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2020
Routes d'admission2
Résumé présentoui

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