Recommendations on the clinical management of the COVID-19 infection by the «new coronavirus» SARS-CoV2. Spanish Paediatric Association working group

Calvo, Cristina; López‐Hortelano, Milagros García; Vicente, Juan Carlos de; Martínez, José Luis

doi:10.1016/j.anpede.2020.02.002

Cited by 46 publications

(36 citation statements)

References 29 publications

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“…In our series, their outcome was similar to healthy children. Although the role of hydroxychloroquine in the treatment of COVID-19 remains to be clarified, we used it in a high percentage of patients following local recommendations, 5 without side effects.…”

Section: Postscriptmentioning

confidence: 99%

SARS-CoV-2 infection in ambulatory and hospitalised Spanish children

et al. 2020

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Section: Postscriptmentioning

confidence: 99%

SARS-CoV-2 infection in ambulatory and hospitalised Spanish children

et al. 2020

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“…Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus that is enveloped with a large, single-stranded RNA genome. It emerged in Wuhan, China in December 2019 and caused the greatest pandemic of the millennium [1]. According to the World Health Organization (WHO) report, the total number of people infected by the disease as of 27 July 2020 is 16,114,449 with 646,641 deaths.…”

Section: Introductionmentioning

confidence: 99%

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

et al. 2020

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The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

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“…SARS-CoV-2 affects children, but multiple studies and previous publications have shown how it produces a milder and shortened condition in pediatric patients [11][12][13][14][15][16][17]. In many cases, children with COVID-19 remain asymptomatic and when symptoms appear, pediatric patients usually present a greater clinical variability.…”

Section: Introductionmentioning

confidence: 99%

ECIEN-2020 study: the effect of COVID-19 on admissions for non-COVID-19 diseases

et al. 2021

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Background The pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2) has had great effects on health systems worldwide, not only in relation to coronavirus disease 2019 (COVID-19) cases but also affecting patients with other pathologies. Methods ECIEN-2020 is an observational study conducted in a tertiary referral hospital in Navarra, Spain. It describes the effects of COVID-19 pandemic and the preventive measures adopted, in pediatric admissions for non-COVID-19 diseases. Admissions during March-June 2020 (first wave of the COVID-19 pandemic in Spain) are described and compared with the same quarter in 2019. A sub-analysis was performed delving into epidemiology. Patient characteristics (age, sex, past medical history), disease characteristics (symptoms, duration of symptoms, previous consultation in Primary Care Health Center), and admission characteristics (place and average stay) were analyzed. Results A 33% reduction in the number of pediatric hospital admissions was observed, decreasing from 529 hospitalizations in 2019 to 353 in 2020 (P < 0.001). This highlights a 48% reduction in patients admitted for pulmonary diseases. There were no significant changes in average hospital-stay, percentage of intensive care unit admissions, or in admissions for other reasons. Percentage of patients admitted among those seen in the emergency department rose from 5.1% in 2019 to 10.9% in 2020, whereas the total number of consultations in the emergency department decreased by 68%. Conclusion The pandemic and the measures adopted due to SARS-CoV-2 have significantly decreased pediatric admissions for non-COVID-19 diseases, especially due to a reduction in the hospitalization for respiratory diseases.

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Recommendations on the clinical management of the COVID-19 infection by the «new coronavirus» SARS-CoV2. Spanish Paediatric Association working group

Cited by 46 publications

References 29 publications

SARS-CoV-2 infection in ambulatory and hospitalised Spanish children

SARS-CoV-2 infection in ambulatory and hospitalised Spanish children

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19

ECIEN-2020 study: the effect of COVID-19 on admissions for non-COVID-19 diseases

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