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

Author

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

Subjects
machine learning, microbiology, deep learning, paediatrics, COVID-19, epidemiology, SARS-CoV-2
Abstract

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 (

Published
2021

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

Author

Jesús M. Antoñanzas, Aida Perramon, Cayetana López, Mireia Boneta, Cristina Aguilera, Ramon Capdevila, Anna Gatell, Pepe Serrano, Miriam Poblet, Dolors Canadell, Mònica Vilà, Georgina Catasús, Cinta Valldepérez, Martí Català, Pere Soler-Palacín, Clara Prats, Antoni Soriano-Arandes, the COPEDI-CAT Research Group, Institut Català de la Salut, [Antoñanzas JM, López C, Boneta M, Aguilera C] Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), Barcelona, Spain. [Perramon A] Department of Physics, Universitat Politècnica de Catalunya (UPC⋅BarcelonaTech), Barcelona, Spain. [Capdevila R] ABS Borges Blanques, Institut Català de Salut (ICS), Lleida, Spain. [Soler-Palacín P, Soriano-Arandes A] Unitat de Patologia Infecciosa i Immunodeficiències de Pediatria, Vall d’Hebron Hospital Universitari, Barcelona, Spain, Vall d'Hebron Barcelona Hospital Campus, Universitat Politècnica de Catalunya. Doctorat en Física Computacional i Aplicada, Universitat Politècnica de Catalunya. Departament de Física, and Universitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos

Subjects
Male, Adolescent, Epidemiology, Otros calificadores::/diagnóstico [Otros calificadores], diagnóstico::técnicas y procedimientos diagnósticos::técnicas de laboratorio clínico::pruebas de bioquímica clínica [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], COVID-19 (Malaltia), Microbiology, Article, paediatrics, Diagnosis::Diagnostic Techniques and Procedures::Clinical Laboratory Techniques::Clinical Chemistry Tests [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], COVID-19 Testing, COVID-19 (Disease), Predictive Value of Tests, Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning [PHENOMENA AND PROCESSES], Virology, Machine learning, Aprenentatge automàtic, Other subheadings::/diagnosis [Other subheadings], conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático [FENÓMENOS Y PROCESOS], virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus [ENFERMEDADES], Humans, Child, Models, Statistical, SARS-CoV-2, Infant, Newborn, Infant, deep learning, COVID-19, Deep learning, Paediatrics, Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections [DISEASES], QR1-502, machine learning, epidemiology, microbiology, Infectious Diseases, Diagnòstic de laboratori, Child, Preschool, Ciències de la salut::Medicina::Medicina comunitària i salut pública [Àrees temàtiques de la UPC], Female, COVID-19 (Malaltia) - Diagnòstic
Abstract

COVID-19; Microbiology; Paediatrics COVID-19; Microbiología; Pediatría COVID-19; Microbiologia; Pediatria 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 (

Published
2022

3. Multisystem inflammatory syndrome in children and SARS-CoV-2 variants: a two-year ambispective multicentric cohort study in Catalonia, Spain.

Author

Pino R, Antoñanzas JM, Paredes-Carmona F, Perramon A, Rivière JG, Coma M, Martínez-Mejías A, Ripoll F, López N, Conti R, Sala-Castellví P, Ruiz M, Brio S, García-Lorenzo M, Esteller M, Carreras-Abad C, Herrero-Hernando C, Schneider SO, Gatell A, Aguilar I, Cantero J, Ruiz G, Fenollosa T, Lobato Z, Villalobos P, Mora E, Anton J, Visa-Reñé N, Soler-Palacin P, Calavia O, Esquirol-Herrero C, Guarch-Ibañez B, García-García JJ, Coma E, Fina F, Prats C, and Soriano-Arandes A

Subjects
Male, Humans, Female, Spain epidemiology, Cohort Studies, SARS-CoV-2, COVID-19 diagnosis, COVID-19 epidemiology
Abstract

Multisystem inflammatory syndrome in children (MIS-C) is a rare but severe disease temporarily related to SARS-CoV-2. We aimed to describe the epidemiological, clinical, and laboratory findings of all MIS-C cases diagnosed in children < 18 years old in Catalonia (Spain) to study their trend throughout the pandemic. This was a multicenter ambispective observational cohort study (April 2020-April 2022). Data were obtained from the COVID-19 Catalan surveillance system and from all hospitals in Catalonia. We analyzed MIS-C cases regarding SARS-CoV-2 variants for demographics, symptoms, severity, monthly MIS-C incidence, ratio between MIS-C and accumulated COVID-19 cases, and associated rate ratios (RR). Among 555,848 SARS-CoV-2 infections, 152 children were diagnosed with MIS-C. The monthly MIS-C incidence was 4.1 (95% CI: 3.4-4.8) per 1,000,000 people, and 273 (95% CI: 230-316) per 1,000,000 SARS-CoV-2 infections (i.e., one case per 3,700 SARS-CoV-2 infections). During the Omicron period, the MIS-C RR was 8.2 (95% CI: 5.7-11.7) per 1,000,000 SARS-CoV-2 infections, which was significantly lower (p < 0.001) than that for previous variant periods in all age groups. The median [IQR] age of MIS-C was 8 [4-11] years, 62.5% male, and 80.2% without comorbidities. Common symptoms were gastrointestinal findings (88.2%) and fever > 39 °C (81.6%); nearly 40% had an abnormal echocardiography, and 7% had coronary aneurysm. Clinical manifestations and laboratory data were not different throughout the variant periods (p > 0.05).  Conclusion: The RR between MIS-C cases and SARS-CoV-2 infections was significantly lower in the Omicron period for all age groups, including those not vaccinated, suggesting that the variant could be the main factor for this shift in the MISC trend. Regardless of variant type, the patients had similar phenotypes and severity throughout the pandemic. What is Known: • Before our study, only two publications investigated the incidence of MIS-C regarding SARS-CoV-2 variants in Europe, one from Southeast England and another from Denmark. What is New: • To our knowledge, this is the first study investigating MIS-C incidence in Southern Europe, with the ability to recruit all MIS-C cases in a determined area and analyze the rate ratio for MIS-C among SARS-CoV-2 infections throughout variant periods. • We found a lower rate ratio of MISC/infections with SARS-CoV-2 in the Omicron period for all age groups, including those not eligible for vaccination, suggesting that the variant could be the main factor for this shift in the MISC trend., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2023
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