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Enregistrement W3155618875 · doi:10.1093/neuonc/noab189

COVID-19 in pediatric cancer: Where are the brain tumors?

2021· letter· en· W3155618875 sur OpenAlexaff
Rebecca Ronsley, Éric Bouffet

Notice bibliographique

RevueNeuro-Oncology · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensHospital for Sick ChildrenBC Children's Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicinePediatric oncologyPediatric cancerPandemicImmunosuppressionCancerCoronavirus disease 2019 (COVID-19)DiseasePediatricsOncologyInternal medicineIntensive care medicineInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Recently, Carai et al published a report highlighting the risk of delayed diagnosis of pediatric central nervous system (CNS) tumors during the novel coronavirus disease 2/SARS-CoV-2 2019 (COVID-19) virus pandemic.1 In this article and others,2 the authors highlight the fragility of pediatric patients with CNS tumors and the importance of early diagnosis and optimal care delivery, which may have been impacted during the pandemic. CNS tumors are the largest group of solid tumors within pediatric oncology patients, accounting for 20%-25% of all childhood cancers. Treatment is variable in its intensity and potential for immunosuppression primarily depends on the tumor type. The risk of COVID-19 to this tumor group is not clear from current publications and here we reviewed the literature to identify pediatric CNS tumors patients reported to have COVID-19 and their outcomes. Through review of PubMed, Google Scholar, and the International Society for Pediatric Oncology (SIOP) and St. Jude Children’s Research Hospital COVID-19 website, we identified 18 published studies describing COVID-19 cases in pediatric oncology patients worldwide (Table 1). Within these studies, there are 551 reported cases of COVID-19 in pediatric oncology patients of which 31 (5.6%) are patients with CNS tumors. Within all cases, reported rate of severe illness or hospitalization requirement ranged from 5% to 84%. Within these hospitalized cases, 40%-86% were already hospitalized for planned oncologic therapy. Within the 551 total cases, there were 26 deaths, of which 6 were attributed to COVID-19, and the remaining 20 the authors attributed to cancer or oncologic therapy-related mortality. No deaths were reported within the patients with brain tumors in these publications. Reported COVID-19 Infections in Pediatric Oncology Patients Abbreviations: B-ALL, B-cell acute lymphoblastic leukemia; CNS, central nervous system; HSCT, hematopoietic stem cell transplantation; ICU, intensive care unit. aSix adult patients were included in this cohort. The authors do not specify whether these patients had brain tumors. bDuplicate cases within. cSee Supplementary Material. Reported COVID-19 Infections in Pediatric Oncology Patients Abbreviations: B-ALL, B-cell acute lymphoblastic leukemia; CNS, central nervous system; HSCT, hematopoietic stem cell transplantation; ICU, intensive care unit. aSix adult patients were included in this cohort. The authors do not specify whether these patients had brain tumors. bDuplicate cases within. cSee Supplementary Material. Based on these publications, children with cancer do not seem to have a higher rate of mortality from COVID-19 than immunocompetent children. This finding is difficult to fully understand in this population with such significant comorbidities, which may independently result in death. The mortality rate from COVID-19 for these combined reports was 1.1%. Recently, Vijenthira et al summarized 34 reports that included predominantly adult oncology patients with concurrent COVID-19 infections. A small proportion of these patients were children in which the rate of mortality attributed to COVID-19 was 4.0%.3 CNS tumors are significantly underrepresented within published cases of COVID-19 in children with cancer. The reasons for this are unclear. This may reflect that most therapies for CNS tumor patients are not significantly myelosuppressive with hospital admissions due to complications occurring less often than in other pediatric oncology patients.4 To support this hypothesis, medulloblastoma, which is usually treated with inpatient chemotherapy, is the most common tumor within patients with comorbid COVID-19. Conversely, there are no reported cases of COVID-19 in children with low-grade gliomas in the current literature. This finding may also reflect hospital COVID-19 testing practices which identify more asymptomatic patients who are admitted for scheduled oncology therapy. Within CNS tumors in the studies included in this review, there were no COVID-19-related deaths reported. Steroids are a mainstay of therapy for severe COVID-19 pneumonitis and improve respiratory symptoms in infected patients. It is not clear whether the frequent use of concurrent steroid therapy in patients with CNS tumors may have an impact on rates and severity of COVID-19 illness. No denominator exists with which to assess the proportion of pediatric patients with CNS tumors within the authors’ practices or specific country. As well, published data may not reflect global COVID-19 cases and the influence of the ongoing global vaccination program on COVID-19 in pediatric oncology patients is not known. SIOP, in collaboration with St. Jude Children’s Research Hospital, has developed a global registry of cases of laboratory-confirmed COVID-19 in children with cancer, and analysis of this data is currently underway.5 Early observations from this registry suggest a higher proportion of asymptomatic patients in the CNS tumor population compared to other malignancies (St. Jude Registry, accessed: May 22, 2021). This registry may provide more comprehensive, large-scale data describing COVID-19 in oncology patients, which is needed to understand the rates and risk of infection in this population. Furthermore, more robust clinical data will allow us to gain insight into the effect on diagnosis and delivery of oncology care. Conflict of interest statement. The authors have no conflicts of interest to disclose.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,059
Score d'incertitude au seuil0,060

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,026
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0070,004
Communication savante0,0050,006
Science ouverte0,0020,005
Intégrité de la recherche0,0590,050
Charge utile insuffisante (le modèle a refusé de juger)0,0110,004

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.

Tête enseignante Opus0,071
Tête enseignante GPT0,399
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2021
Routes d'admission1
Résumé présentoui

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