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Enregistrement W3203047143 · doi:10.2196/29049

Clinical Characteristics of Children With COVID-19 in the United Arab Emirates: Cross-sectional Multicenter Study

2021· article· en· W3203047143 sur OpenAlexvenueno aff
Farah Ennab, Mariam ElSaban, Eman Khalaf, Hanieh Sadat Tabatabaei Yeganeh, Amar Hassan Khamis, Bindu Radha Devi, Kashif Hanif, Hiba Elhassan, Ketharanathan Saravanan, David Cremonesini, Rizwana Popatia, Zainab Malik, Samuel B. Ho, Rania Abusamra

Notice bibliographique

RevueJMIR Pediatrics and Parenting · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 Clinical Research Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineCross-sectional studyrhinorrheaAsymptomaticPediatricsPandemicCoronavirus disease 2019 (COVID-19)Multicenter studyPopulationInternal medicineSurgeryEnvironmental healthInfectious disease (medical specialty)Disease

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: COVID-19 has infected over 123 million people globally. The first confirmed case in the United Arab Emirates (UAE) was reported on January 29, 2020. According to studies conducted in the early epicenters of the pandemic, COVID-19 has fared mildly in the pediatric population. To date, there is a lack of published data about COVID-19 infection among children in the Arabian region. OBJECTIVE: This study aims to investigate the clinical characteristics, laboratory findings, treatment, and outcomes of children with COVID-19. METHODS: This cross-sectional, multicenter study included children with confirmed COVID-19 infection admitted to 3 large hospitals in Dubai, UAE, between March 1 and June 15, 2020. Serial COVID-19 polymerase chain reaction (PCR) testing data were collected, and patients' demographics, premorbid clinical characteristics, and inpatient hospital courses were examined. RESULTS: In all, 111 children were included in our study and represented 22 nationalities. Of these, 59 (53.2%) were boys. The mean age of the participants was 7 (SD 5.3) years. About 15.3% of children were younger than 1 year. Only 4 (3.6%) of them had pre-existing asthma, all of whom had uneventful courses. At presentation, of the 111 children, 43 (38.7%) were asymptomatic, 68 (61.2%) had mild or moderate symptoms, and none (0%) had severe illness requiring intensive care. Fever (23/111, 20.7%), cough (22/111, 19.8%), and rhinorrhea (17/111, 15.3%) were the most common presenting symptoms, and most reported symptoms resolved by day 5 of hospitalization. Most patients had no abnormality on chest x-ray. The most common laboratory abnormalities on admission included variations in neutrophil count (22/111, 24.7%), aspartate transaminase (18/111, 22.5%), alkaline phosphatase (29/111, 36.7%), and lactate dehydrogenase (31/111, 42.5%). Children were infrequently prescribed targeted medications, with only 4 (3.6%) receiving antibiotics. None of the 52 patients tested for viral coinfections were positive. COVID-19 PCR testing turned negative at a median of 10 days (IQR: 6-14) after the first positive test. Overall, there was no significant difference of time to negative PCR results between symptomatic and asymptomatic children. CONCLUSIONS: This study of COVID-19 presentations and characteristics presents a first look into the burden of COVID-19 infection in the pediatric population in the UAE. We conclude that a large percentage of children experienced no symptoms and that severe COVID-19 disease is uncommon in the UAE. Various laboratory abnormalities were observed despite clinical stability. Ongoing surveillance, contact tracing, and public health measures will be important to contain future outbreaks.

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 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)0,002
score de la tête « metaresearch » (Gemma)0,019
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
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 enseignants0,017
Score d'incertitude au seuil0,989

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,019
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 recherche0,0000,001
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 Opus0,103
Tête enseignante GPT0,470
Écart entre enseignants0,367 · 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 tête enseignante, pas un consensus.

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

Citations8
Publié2021
Routes d'admission1
Résumé présentoui

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