Are patients with cancer at higher risk of COVID-19-related death? A systematic review and critical appraisal of the early evidence
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Notice bibliographique
Résumé
BACKGROUND: Early reports suggested that COVID-19 patients with cancer were at higher risk of COVID-19-related death. We conducted a systematic review with risk of bias assessment and synthesis of the early evidence on the risk of COVID-19-related death for COVID-19 patients with and without cancer. METHODS AND FINDINGS: We searched Medline/Embase/BioRxiv/MedRxiv/SSRN databases to 1 July 2020. We included cohort or case-control studies published in English that reported on the risk of dying after developing COVID-19 for people with a pre-existing diagnosis of any cancer, lung cancer, or haematological cancers. We assessed risk of bias using tools adapted from the Newcastle-Ottawa Scale. We used the generic inverse-variance random-effects method for meta-analysis. Pooled odds ratios (ORs) and hazard ratios (HRs) were calculated separately. Of 96 included studies, 54 had sufficient non-overlapping data to be included in meta-analyses (>500,000 people with COVID-19, >8000 with cancer; 52 studies of any cancer, three of lung and six of haematological cancers). All studies had high risk of bias. Accounting for at least age consistently led to lower estimated ORs and HRs for COVID-19-related death in cancer patients (e.g. any cancer versus no cancer; six studies, unadjusted OR=3.30,95%CI:2.59-4.20, adjusted OR=1.37,95%CI:1.16-1.61). Adjusted effect estimates were not reported for people with lung or haematological cancers. Of 18 studies that adjusted for at least age, 17 reported positive associations between pre-existing cancer diagnosis and COVID-19-related death (e.g. any cancer versus no cancer; nine studies, adjusted OR=1.66,95%CI:1.33-2.08; five studies, adjusted HR=1.19,95%CI:1.02-1.38). CONCLUSIONS: The initial evidence (published to 1 July 2020) on COVID-19-related death in people with cancer is characterised by multiple sources of bias and substantial overlap between data included in different studies. Pooled analyses of non-overlapping early data with adjustment for at least age indicated a significantly increased risk of COVID-19-related death for those with a pre-existing cancer diagnosis.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découle