Symptom-Based Predictive Model of COVID-19 Disease in Children

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms....

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Bibliographic Details
Published in:	Viruses Vol. 14; no. 1; p. 63
Main Authors:	Antoñanzas, Jesús M, Perramon, Aida, López, Cayetana, Boneta, Mireia, Aguilera, Cristina, Capdevila, Ramon, Gatell, Anna, Serrano, Pepe, Poblet, Miriam, Canadell, Dolors, Vilà, Mònica, Catasús, Georgina, Valldepérez, Cinta, Català, Martí, Soler-Palacín, Pere, Prats, Clara, Soriano-Arandes, Antoni, The Copedi-Cat Research Group
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 30-12-2021 MDPI
Subjects:	Adolescent Artificial intelligence Bacterial infections Child Child, Preschool Children Children & youth Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - epidemiology COVID-19 Testing - methods Decision making deep learning Diagnosis Diagnostic tests Disease Epidemiology Female Fever Humans Infant Infant, Newborn Infections Learning algorithms Machine Learning Male Medical research Models, Statistical Olfaction paediatrics Pandemics Pediatrics Population Prediction models Predictive Value of Tests Primary care Questionnaires SARS-CoV-2 SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome coronavirus 2 Statistical analysis Systematic review Teenagers Viral infections Viruses Spain COVID-19 paediatrics deep learning epidemiology SARS-CoV-2 microbiology machine learning
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Summary:	Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms. Epidemiological and clinical data were obtained from the REDCap registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset. The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children. Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. Collaborators/Membership of the Group Team Name is provided in the Supplementary Material.
ISSN:	1999-4915 1999-4915
DOI:	10.3390/v14010063