Concomitant infections in patients with cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) study.
Notice bibliographique
Résumé
6561 Background: COVID-19 has been associated with immune modulation that may predispose infected patients to bacterial, viral, or fungal co-infections. Due to critical illness, > 70% of patients with severe COVID-19 receive empiric antibacterial or antifungal therapy, along with standard anti-COVID-19 treatments. However, the frequency of proven or probable secondary infections is < 10%. To our knowledge, there are no studies evaluating co-infections in patients with cancer and COVID-19, a vulnerable group with multiple risk factors for co-infections. We aim to describe the prevalence of bacterial, viral, and fungal co-infections, identify risk factors for coinfection, and investigate the potential impact of co-infections on mortality, in patients with a history of cancer and COVID-19. Methods: The CCC19 registry (NCT04354701) includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. We captured bacterial, fungal, or viral co-infections diagnosed within ±2 weeks from diagnosis of COVID-19, identified factors associated with an increased risk of having a co-infection, and evaluated the association of co-infections with 30-day all-cause mortality. Results: We examined 6732 patients with a history of cancer and a laboratory-confirmed diagnosis of SARS-CoV-2 reported to CCC19 by 82 sites between March 17, 2020 and February 3, 2021, with complete data on coinfection status. Median age was 65 (interquartile range: 55-75) years with 48% male, 52% non-Hispanic white, 19% non-Hispanic black, and 16% Hispanic. 5448 (81%) had solid tumors and 1466 (22%) had hematologic malignancies. Bacterial infections were reported in 823 patients (12%), including 296 Gram+ and 245 Gram- bacterial events. Documented viral (176 patients, 3%) and fungal (59 patients, 0.9%) co-infections were rare. The risk for co-infections increased with age, and they were more frequent among men, older patients, and those with diabetes, pulmonary or renal comorbid conditions, active progressive cancer, or hematologic malignancies (unadjusted P< 0.01). The frequency of reported co-infections decreased over the study period (divided into quartiles, Mantel-Haenszel P< 0.01). All-cause mortality rates were higher among those with bacterial (24% vs. 10%), viral (22% vs. 12%), and fungal (37% vs. 12%) coinfections compared to those without (unadjusted P< 0.01). Conclusions: The frequency of bacterial infections in patients with cancer and COVID-19 is relatively low. Viral and fungal co-infections are uncommon. Co-infections are associated with higher mortality rates. Several patient and tumor factors can be used for risk stratification and guide early empiric antimicrobial agent selection, which may improve clinical outcomes. These data could inform antimicrobial stewardship interventions in this tenuous patient population.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».