Abstract S12-01: High mortality among hospital-acquired COVID-19 infection in patients with cancer: An observational cohort study from Quebec and British Columbia
Notice bibliographique
Résumé
Abstract Background: Studies suggest that patients with cancer are more likely to experience severe outcomes from COVID-19. Therefore, cancer centers have undertaken efforts to care for patients with cancer in COVID-free zones. Nevertheless, nosocomial transmission of COVID-19 in patients with cancer likely occurs, but the frequency and relevance of these events remain unknown. The goal of this study was to determine the incidence and impact of hospital-acquired COVID-19 in this population and identify prognostic factors for COVID-19 severity in patients with cancer. Methods: Patients with cancer and a laboratory-confirmed or presumed diagnosis of COVID-19 were prospectively identified using provincial registries and hospital databases between March 3rd and May 23rd, 2020, in the provinces of Quebec and British Columbia. Patients’ baseline characteristics including age, sex, comorbidities, cancer type, and type of anticancer treatment were collected. The primary outcome was incidence of hospital-acquired infection defined by diagnosis of SARS-CoV-2 5 days after hospital admission for COVID-unrelated cause. Co-primary outcomes were death or composite outcomes of severe illness from COVID-19 such as hospitalization, supplemental oxygen, intensive-care unit (ICU) admission, and/or mechanical ventilation. Results: A total of 253 patients (N=250 adult and N=3 pediatric) with COVID-19 and cancer were identified, and the majority were residents of Quebec (N=236). Ninety patients (35.6%) received active anticancer treatment in the last 3 months prior to COVID-19 diagnosis. During a median follow-up of 23 days, 209 (82.6%) required hospitalization, 38 (15%) required admission to ICU, and 71 (28%) died. Forty-seven (19%) had a diagnosis of hospital-acquired COVID-19. Median overall survival was shorter in those with hospital-acquired infection, compared to a contemporary community-acquired population (27 days vs. 71 days, HR 2.2, 95% CI 1.2-4.0, p=0.002). Multivariate analysis demonstrated that hospital-acquired COVID-19, age, ECOG status, and advanced stage of cancer were independently associated with death. Conclusion: Our study demonstrates a high rate of nosocomial transmission of COVID-19, associated with increased mortality in both univariate and multivariate analysis in the cancer population, reinforcing the importance of treating patients with cancer in COVID-free zones. We also validated that age, poor ECOG, and advanced cancer were negative prognostic factors for COVID-19 in patients with cancer. Citation Format: Arielle Elkrief, Antoine Desilets, Neha Papneja, Lena Cvetkovic, Catherine Groleau, Yahia Abdelali Lakehal, Layla Shbat, Corentin Richard, Julie Malo, Wiam Belkaid, Erin Cook, Stephane Doucet, Thai Hoa Tran, Patrice Savard, Kevin Jao, Nathalie Daaboul, Eric Bhang, Jonathan Loree, Wilson Miller, Donald Vinh, Nathaniel Bouganim, Gerald Batist, Caroline Letendre, Bertrand Routy. High mortality among hospital-acquired COVID-19 infection in patients with cancer: An observational cohort study from Quebec and British Columbia [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S12-01.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».