Symptom-Based Predictive Model of COVID-19 Disease in Children.
Por:
Antoñanzas JM, Perramon A, López C, Boneta M, Aguilera C, Capdevila R, Gatell A, Serrano P, Poblet M, Canadell D, Vilà M, Catasús G, Valldepérez C, Català M, Soler-Palacín P, Prats C, Soriano-Arandes A and The Copedi-Cat Research Group
Publicada:
30 dic 2021
Ahead of Print:
30 dic 2021
Resumen:
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Filiaciones:
Antoñanzas JM:
Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08034 Barcelona, Spain
Perramon A:
Department of Physics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08028 Barcelona, Spain
López C:
Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08034 Barcelona, Spain
Boneta M:
Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08034 Barcelona, Spain
Aguilera C:
Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08034 Barcelona, Spain
Capdevila R:
ABS Borges Blanques, Institut Català de Salut (ICS), 25400 Lleida, Spain
Gatell A:
Equip Pediatria Territorial Alt Penedès-Garraf, Institut Català de Salut (ICS), 28036 Barcelona, Spain
Serrano P:
Equip Pediatria Territorial Alt Penedès-Garraf, Institut Català de Salut (ICS), 28036 Barcelona, Spain
Poblet M:
Equip Territorial Pediàtric Sabadell Nord, Institut Català de Salut (ICS), 08206 Barcelona, Spain
Canadell D:
CAP Barberà del Vallés, 08210 Barcelona, Spain
Vilà M:
EAP Horta, 08024 Barcelona, Spain
Catasús G:
CAP Drassanes, 08001 Barcelona, Spain
Valldepérez C:
Equip Pediatria Territorial Alt Penedès-Garraf, Institut Català de Salut (ICS), 28036 Barcelona, Spain
:
Department of Physics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08028 Barcelona, Spain
Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), 58525 Badalona, Spain
Soler-Palacín P:
Paediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
Prats C:
Department of Physics, Universitat Politècnica de Catalunya (UPC·BarcelonaTech), 08028 Barcelona, Spain
Soriano-Arandes A:
Paediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
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