Abstract S09-03: Factors affecting COVID-19 outcomes in cancer patients: A first report from Guy’s Cancer Centre in London
Notice bibliographique
Résumé
Abstract Background: Current precautionary management decisions being made for cancer patients are based on assumptions supported by limited evidence, based on small case series from China and Italy and larger series from New York and a recent consortium of 900 patients from over 85 hospitals in the USA, Canada, and Spain. Hence, 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)), dyspnea (2.60 (1.00-6.76)), gastrointestinal 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), dyspnea (4.94 (1.99-12.25), and increased CRP levels (10.35 (1.05-52.21)) were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin (0.04 (0.01-0.04). Conclusions: Our analysis of one of the largest single-center series of COVID-19-positive cancer patients to date confirms a similar distribution of age, sex, and comorbidities as reported for other populations. With respect to cancer-specific observations, patients who have lived longer with their cancer were found to be more susceptible to a greater infection severity, possibly reflecting the effect of more advanced malignant disease, as almost half of the severe cohort were on third-line metastatic treatment, or the impact of this infection. The latter was also found to be associated with COVID-19 death in cancer patients, as were Asian ethnicity and palliative treatment. Further validation will be provided from other large case series, as well as from those including longer follow-up, to provide more definite guidance for oncologic care. Citation Format: Beth Russell, Charlotte Moss, Sophie Papa, Sheeba Irshad, Paul Ross, James Spicer, Shahram Kordasti, Danielle Crawley, Harriet Wylie, Fidelma Cahill, Anna Haire, Kamarul Zaki, Fareen Rahman, Ailsa Sita-Lumsden, Debra Josephs, Deborah Enting, Mary Lei, Sharmista Ghosh, Claire Harrison, Angela Swampillai, Richard Sullivan, Anne Rigg, Saoirse Dolly, Mieke Van Hemelrijck, Guy's Cancer Real World Evidence Programme. Factors affecting COVID-19 outcomes in cancer patients: A first report from Guy’s Cancer Centre in London [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 S09-03.
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 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».