MétaCan
Menu
← Retour à la cohorte
Enregistrement W3212269438 · doi:10.1182/blood-2021-149652

COVID-19 Vaccine Response in Patients with Hematologic Malignancy: A Systematic Review and Meta-Analysis

2021· review· en· W3212269438 sur OpenAlexaff
Inna Y. Gong, Abi Vijenthira, Stephen Betschel, Lisa K. Hicks, Matthew C. Cheung

Notice bibliographique

RevueBlood · 2021
Typereview
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensInstitute for Clinical Evaluative SciencesHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalPrincess Margaret Cancer CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineHematologic malignancyMalignancyInternal medicineMeta-analysisCancerVaccinationPopulationImmunology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Emerging data suggests that seroresponse (SR) in patients with hematologic malignancy following COVID-19 vaccination is likely lower than in patients without blood cancer. The objective of this study was to perform a systematic review and meta-analysis on SR in patients with hematologic malignancy who received COVID-19 vaccination (submitted to PROSPERO for registration). Methods: We searched PubMed and EMBASE from December 1, 2020, to July 22, 2021, to identify studies of SR following COVID-19 vaccine in adult patients with hematologic malignancy (including studies in which patients with hematologic malignancy represented a subset of a broader population). Patients with positive serologic response at baseline (prior to vaccination) or known COVID-19 infection were excluded. The primary outcomes were pooled SR estimates following COVID-19 vaccination in patients with hematologic malignancy, and pooled SR estimates of subgroups based on hematologic malignancy type. Secondary outcomes were pooled relative risk ratio (RR; compared to non-cancer controls) based on dichotomous-effect SR in all patients, and subgroups based on hematologic malignancy type, treatment status, and use of anti-CD20 therapy. Pooled estimates and RR with its associated 95% confidence intervals (CIs) were calculated using MetaXL (EpiGear), and Reference Manager (Cochrane) using random effects model. Results A total of 17 studies comprising 2834 patients with hematologic malignancy from Europe, United Kingdom and North America were included (Figure 1). The pooled estimate for SR was 58% (95% CI 48-67%, I 2 95%), with a RR of 0.53 (95% 0.42-0.66, I 2 94%) when compared to controls (10 studies with comparison group, 1092 hematologic malignancy patients, 830 controls; Figure 2). The pooled estimate for SR varied by type of hematologic malignancy: lymphomas SR 52% (95% CI 36-68%, 7 studies, 832 patients, I 2 94%); chronic lymphocytic leukemia (CLL) SR 42% (95% CI 25-60%, 6 studies, 921 patients, I 2 93%); plasma cell dyscrasias SR 66% (95% CI 47-83%, 8 studies, 611 patients, I 2 95%); myeloproliferative neoplasms (MPNs, including chronic myelogenous leukemia) SR 83% (95% CI 68-95%, 6 studies, 227 patients, I 2 58%); acute leukemia SR 86% (95% CI 77-94%, 2 studies, 67 patients, 46 acute myelogenous leukemia [AML] and 15 acute lymphocytic leukemia], I 2 0%). The RR for SR also varied by type of hematologic malignancy: lymphomas (excluding CLL) RR 0.48 (95% CI 0.34-0.68, 4 studies, 337 patients, I 2 89%); CLL RR 0.37 (95% CI 0.25-0.53, 3 studies, 194 patients, I 2 54%); plasma cell dyscrasias RR 0.73 (95% CI 0.62-0.86, 5 studies, 323 patients, I 2 70%); RR MPN 0.78 (95% CI 0.62-0.99, 3 studies, 199 patients, I 2 90%). The pooled estimate for SR in those receiving treatment was 42% (95% CI 26-58%, 9 studies, 683 patients, I 2 94%). The pooled estimates for SR for those receiving anti-CD20, bruton tyrosine kinase inhibitor (BTKi), or venetoclax were 13% (95% CI 1-32%, 6 studies, 367 patients, I 2 88%), 42% (95% CI 17-71%, 3 studies, 319 patients, I 2 75%), and 20% (95% CI 0-54%, 3 studies, 39 patients, I 2 66%), respectively. The RR for those receiving treatment for their hematologic malignancy compared to those who were not receiving treatment was 0.51 (95% CI 0.37-0.71, 8 studies, 579 patients, I 2 89%; Figure 3). . The RR of patients receiving anti-CD20 therapy compared to non-cancer controls was 0.13 (95% CI 0.02-0.93, 102 patients, I 2 73%). For patients treated with anti-CD20 therapy, the RR of those receiving vaccination within 9-12 months compared to beyond 9-12 months was 0.12 (95% CI 0.06-0.25, 2 studies, 74 patients, I 2 0%; Figure 4). Conclusion: Our systematic review and meta-analysis suggests that patients with hematologic malignancy have a lower SR rate following vaccination compared to controls. Furthermore, SR is variable across different types of hematologic malignancy, with very good response rates seen in patients with myeloid diseases (MPN and AML) and poor response rates seen in lymphoma and CLL. Active treatment, particularly anti-CD20 therapy within 12 months of vaccination, is associated with a particularly low SR following vaccination. Additional studies are needed to understand non-humoral responses to vaccination, and to guide decisions regarding how to optimize vaccine response in patients with blood cancer. We plan to update the systematic review and meta-analysis as more data become available. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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)0,013
score de la tête « metaresearch » (Gemma)0,032
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,067

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,032
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0150,034
Bibliométrie0,0060,007
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0020,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,103
Tête enseignante GPT0,397
Écart entre enseignants0,294 · 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'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

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

En bref

Citations9
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetSARS-CoV-2 and COVID-19 Research→Travaux en français237 207→