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Enregistrement W4391874486 · doi:10.1016/j.ajog.2024.02.008

Maternal vaccination against COVID-19 and neonatal outcomes during Omicron: INTERCOVID-2022 study

2024· article· en· W4391874486 sur OpenAlexaff
Fernando C. Barros, Robert B. Gunier, Albertina Rego, Loı̈c Sentilhes, Stephen Rauch, Serena Gandino, Jagjit S. Teji, Jim Thornton, Alisa Kachikis, Ricardo Nieto, Rachel Craik, Paolo Ivo Cavoretto, Adele Winsey, Paola Roggero, Gabriel B Rodriguez, Valeria Savasi, Erkan Kalafat, Francesca Giuliani, Marta Fabre, Anne Caroline Benski, Irma A. Coronado-Zarco, Stefania Livio, Adela Ostrovska, Nerea Maíz, Fabiola R. Castedo Camacho, Ashley Peterson, Philippe Deruelle, Carolina Giudice, Roberto Casale, Laurent Salomon, Constanza P. Soto Conti, Federico Prefumo, Ehab Zakaria Mohamed Elbayoumy, Marynéa Silva do Vale, Valéria Hernández, Katherine Chandler, Milagros Risso, Emily Marler, Daniela M. Cáceres, Guadalupe Albornoz Crespo, Ernawati Ernawati, Michal Lipschuetz, Shabina Ariff, Ken Takahashi, Carmen Vecchiarelli, Teresa Hubka, Satoru Ikenoue, Gabriela Tavchioska, Babagana Bako, Adejumoke Idowu Ayede, Brenda Eskenazi, Zulfiqar A Bhutta, Stephen Kennedy, Aris T. Papageorghiou, José Villar

Notice bibliographique

RevueAmerican Journal of Obstetrics and Gynecology · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 Impact on Reproduction
Établissements canadiensHospital for Sick Children
Organismes subventionnairesNorgineUniversitas AirlanggaJikei University School of MedicineInstituto Nacional de PerinatologíaUniversità degli Studi di BresciaHebrew University of JerusalemKeio UniversityUniversidade Federal do MaranhãoNational Institute for Health and Care ResearchGlaxoSmithKline
Mots-clésMedicineCoronavirus disease 2019 (COVID-19)Vaccination2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PediatricsVirologyInternal medicineOutbreakDiseaseInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: In early 2023, when Omicron was the variant of concern, we showed that vaccinating pregnant women decreased the risk for severe COVID-19-related complications and maternal morbidity and mortality. OBJECTIVE: This study aimed to analyze the impact of COVID-19 during pregnancy on newborns and the effects of maternal COVID-19 vaccination on neonatal outcomes when Omicron was the variant of concern. STUDY DESIGN: INTERCOVID-2022 was a large, prospective, observational study, conducted in 40 hospitals across 18 countries, from November 27, 2021 (the day after the World Health Organization declared Omicron the variant of concern) to June 30, 2022, to assess the effect of COVID-19 in pregnancy on maternal and neonatal outcomes and to assess vaccine effectiveness. Women diagnosed with laboratory-confirmed COVID-19 during pregnancy were compared with 2 nondiagnosed, unmatched women recruited concomitantly and consecutively during pregnancy or at delivery. Mother-newborn dyads were followed until hospital discharge. The primary outcomes were a neonatal positive test for COVID-19, severe neonatal morbidity index, severe perinatal morbidity and mortality index, preterm birth, neonatal death, referral to neonatal intensive care unit, and diseases during the neonatal period. Vaccine effectiveness was estimated with adjustment for maternal risk profile. RESULTS: We enrolled 4707 neonates born to 1577 (33.5%) mothers diagnosed with COVID-19 and 3130 (66.5%) nondiagnosed mothers. Among the diagnosed mothers, 642 (40.7%) were not vaccinated, 147 (9.3%) were partially vaccinated, 551 (34.9%) were completely vaccinated, and 237 (15.0%) also had a booster vaccine. Neonates of booster-vaccinated mothers had less than half (relative risk, 0.46; 95% confidence interval, 0.23-0.91) the risk of being diagnosed with COVID-19 when compared with those of unvaccinated mothers; they also had the lowest rates of preterm birth, medically indicated preterm birth, respiratory distress syndrome, and number of days in the neonatal intensive care unit. Newborns of unvaccinated mothers had double the risk for neonatal death (relative risk, 2.06; 95% confidence interval, 1.06-4.00) when compared with those of nondiagnosed mothers. Vaccination was not associated with any congenital malformations. Although all vaccines provided protection against neonatal test positivity, newborns of booster-vaccinated mothers had the highest vaccine effectiveness (64%; 95% confidence interval, 10%-86%). Vaccine effectiveness was not as high for messenger RNA vaccines only. Vaccine effectiveness against moderate or severe neonatal outcomes was much lower, namely 13% in the booster-vaccinated group (all vaccines) and 25% and 28% in the completely and booster-vaccinated groups, respectively (messenger RNA vaccines only). Vaccines were fairly effective in protecting neonates when given to pregnant women ≤100 days (14 weeks) before birth; thereafter, the risk increased and was much higher after 200 days (29 weeks). Finally, none of the neonatal practices studied, including skin-to-skin contact and direct breastfeeding, increased the risk for infecting newborns. CONCLUSION: When Omicron was the variant of concern, newborns of unvaccinated mothers had an increased risk for neonatal death. Neonates of vaccinated mothers had a decreased risk for preterm birth and adverse neonatal outcomes. Because the protective effect of COVID-19 vaccination decreases with time, to ensure that newborns are maximally protected against COVID-19, mothers should receive a vaccine or booster dose no more than 14 weeks before the expected date of delivery.

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)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,016
Tête enseignante GPT0,329
Écart entre enseignants0,313 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations35
Publié2024
Routes d'admission1
Résumé présentoui

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