Factors affecting COVID-19 outcomes in cancer patients: A first report from Guy’s Cancer Centre in London
Notice bibliographique
Résumé
Abstract Background There is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 due to the lack of large studies. Methods We used data from a single large UK Cancer Centre to assess demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19 diagnosis between 29 February-12 May 2020. Logistic/Cox proportional hazards models were used to identify which demographic and/or clinical characteristics were associated with COVID-19 severity/death. Results 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with severe disease. Initial diagnosis of cancer >24m before COVID-19 (OR:1.74 (95%CI: 0.71-4.26)), presenting with fever (6.21 (1.76-21.99)), dyspnoea (2.60 (1.00-6.76)), gastro-intestinal symptoms (7.38 (2.71-20.16)), or higher levels of CRP (9.43 (0.73-121.12)) were linked with greater COVID-19 severity. During median follow-up of 47d, 34 patients had died of COVID-19 (22%). Asian ethnicity (3.73 (1.28-10.91), palliative treatment (5.74 (1.15-28.79), initial diagnosis of cancer >24m before (2.14 (1.04-4.44), dyspnoea (4.94 (1.99-12.25), and increased CRP levels (10.35 (1.0552.21)) were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin (0.04 (0.01-0.04). Conclusions A longer-established diagnosis of cancer was associated with increasing severity of infection as well as COVID-19 death, possibly reflecting effects of more advanced malignant disease impact on this infection. Asian ethnicity and palliative treatment were also associated with COVID-19 death in cancer patients. Contribution to the field In the context of cancer, the COVID-19 pandemic has led to challenging decision-making. These are supported by limited evidence with small case studies being reported from China, Italy, New York and a recent consortium of 900 patients from over 85 hospitals in the USA, Canada, and Spain. As a result of their limited sample sizes, most studies were not able to distinguish between the effects of age, cancer, and other comorbidities on COVID-19 outcomes. Moreover, the case series from New York analysed which patient characteristics are associated with COVID-19 death, but only made a comparison with non-cancer patients. The first results of the COVID-19 and Cancer Consortium provide insights from a large cohort in terms of COVID-19 mortality, though a wide variety of institutions with different COVID-19 testing procedures were included. Given the current lack of (inter)national guidance for cancer patients in the context of COVID-19, we believe that our large cancer centre can provide an important contribution to the urgent need for further insight into the intersection between COVID-19 and cancer. With comprehensive in-house patient details, consistent inclusion criteria and up-to-date cancer and COVID-19 outcomes, we are in position to provide rapid analytical information to the oncological community.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».