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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 OpenAlexaff
Prakesh S. Shah, Yenge Diambomba, Ganesh Acharya, Shaun K. Morris, Ari Bitnun

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.

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)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

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

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'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

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

En bref

Citations231
Publié2020
Routes d'admission1
Résumé présentoui

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