MétaCan
Menu
Retour à la cohorte
Enregistrement W3015493153 · doi:10.1111/aogs.13870

Classification system and case definition for SARS‐CoV‐2 infection in pregnant women, fetuses, and neonates

2020· article· en· W3015493153 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueActa Obstetricia Et Gynecologica Scandinavica · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 Impact on Reproduction
Établissements canadiensHospital for Sick ChildrenUniversity of TorontoMount Sinai Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicinePregnancyFetusObstetricsMiscarriageGestationTransmission (telecommunications)Pediatrics

Résumé

récupéré en direct d'OpenAlex

The possibility of mother-to-fetus transmission of SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), is currently a highly debated concept in perinatal medicine.1 It has implications for the mother, fetus, and neonate, as well as for healthcare providers present at the time of birth and caring for the child during the neonatal period, including obstetricians, midwives, family doctors, anesthetists, pediatricians, neonatologists, nurses, and respiratory therapists. At present the evidence for intrauterine transmission from mother to fetus or intrapartum transmission from mother to the neonate is sparse. There are limitations associated with sensitivity and specificity of diagnostic tests used and classification of patients based on test results has also been questioned.2-7 As a result, differing recommendations have emerged regarding which samples should be collected and when, and how to distinguish infection from contamination,8-11 making it difficult for clinicians “on the ground” to know which recommendations to follow.12 Additionally, a woman could be infected at any time during pregnancy and the impact on the fetus when maternal infection occurs earlier in pregnancy may be different than when it occurs in the two weeks prior to delivery. Infection during the first or second trimester has the potential to cause miscarriage, preterm birth, birth defects or possibly other features of congenital infection. In late gestation maternal infection, we need to consider the possibility that the newborn could have active infection and consequently at risk of adverse outcomes and also that the infant could pose a risk to healthcare workers. Therefore, in this paper, we focus solely on newborn infants whose mothers have documented or suspected COVID-19 at the time of onset of labor and delivery. Fortunately, the majority of neonates born to mothers with SARS-CoV-2 infection either do not become infected or exhibit mild symptoms at birth. However, the fact that a significant proportion of maternal and neonatal infections can be asymptomatic creates difficulty in ascertaining the disease burden on neonates and the possibility of transmission to healthcare providers during resuscitation or admission to a unit. Unequivocal diagnosis of most fetal or neonatal infections is typically made by detection of the organism in culture or by nucleic acid amplification tests that identify the presence of the pathogen's RNA or DNA in amniotic fluid prior to onset of labor or in properly collected fetal/neonatal blood or body fluid samples, or by histopathological demonstration of the organism in fetal/neonatal tissues. Serology plays an important role in diagnosis for certain congenital infections such as toxoplasmosis and syphilis. The role of serology in the diagnosis of SARS-CoV-2 infection is still uncertain and consequently it is difficult to envision how serology may contribute to newborn diagnosis – especially when maternal infection occurs late in pregnancy and there may not have been sufficient time for antibodies to be generated. Until there is a clear understanding of appropriate diagnostic methods and interpretation of results for newborn infants, a detailed classification system is likely to be helpful. Such a system could aid healthcare practitioners in evaluating patients, determining appropriate infection control measures, planning appropriate follow-up for neonates and infants, allowing large epidemiological studies and helping collaboration between international efforts to learn about potential effects of maternal infections. In this paper, we present such a classification. In developing this system, we adopted an approach similar to Lebech et al13 in creating five mutually exclusive categories of the likelihood of infection: (a) confirmed, (b) probable, (c) possible, (d) unlikely, and (e) not infected. The first and last categories (confirmed and not infected) are to be considered absolute and confirmatory. The probable category denotes strong evidence of infection but a lack of absolute proof. The possible category denotes evidence that is suggestive of infection but is incomplete. The unlikely category applies when there is little support for a diagnosis, but infection cannot be completely ruled out. Notably, a case may be initially assigned to one category and later moved to another category as more information is available. All five categories will not be applicable to all types of infections. We have avoided terminology such as ‘vertical’ or ‘horizontal transmission’ and rather developed a system that classifies transmission as congenital infection in intrauterine death/ stillbirth, congenital infection in live born, neonatal infection acquired intrapartum, or neonatal infection acquired postnatally,14 which aligns with the actual pathological process as opposed to unknown directions of transmission.15 Our classification system is presented in Table 1. Currently, the classification system takes into account the results of maternal testing, clinical status of the neonate at birth, and results of neonatal testing. The criteria suggested are based on current evidence. For the perinatal infection categories, it assumes that maternal status is either definitive or probable and is in the vicinity of childbirth. These categories may need to be modified as a clearer picture of the effects of SARS-CoV-2 infection on developing fetus emerges. We believe that this rapid, easy, and accessible system will also facilitate the development of good clinical practice parameters and guidelines for managing neonates and ensuring safety of families and healthcare providers. This classification system is dependent on the availability of reliable diagnostic tests and emerging methods may lead to its modification. We have not included testing of breast milk, maternal skin swabs, or rectal swabs in the proposed classification as their roles in diagnosing maternal-fetal-neonatal SARS-CoV-2 infections are unclear at this time. We expect refinements to this classification system as additional data become available and further experience is gained. All authors report no actual or potential conflicts of interest.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,189
Score d'incertitude au seuil0,856

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,090
Tête enseignante GPT0,329
Écart entre enseignants0,239 · 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