Pedro E Fleitas1, Jorge A Paz2, Mario I Simoy3, Carlos Vargas4, Rubén O Cimino5, Alejandro J Krolewiecki6, Juan P Aparicio7. 1. MSc, Instituto de Investigaciones de Enfermedades Tropicales (IIET-CONICET), Universidad Nacional de Salta, Alvarado 751, San Ramon de la Nueva Oran, zip code A4530, Salta, Argentina, Cátedra de Química Biología, Facultad de Ciencias Naturales, Universidad Nacional de Salta, Av. Bolivia 5150, zip code A4400, Salta, Argentina. 2. PhD, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Salta, Av. Bolivia 5150, zip code A4400, Salta, Argentina. 3. PhD, Instituto de Investigaciones en Energía no Convencional (INENCO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Salta, Av. Bolivia 5150, zip code A4400, Salta, Argentina, Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable, Universidad Nacional del Centro de la Provincia de Buenos Aires, Pinto 399, zip code B7000, Tandil, Buenos Aires, Argentina. 4. MSc, Instituto de Investigaciones de Enfermedades Tropicales (IIET-CONICET), Universidad Nacional de Salta, Alvarado 751, San Ramon de la Nueva Oran, zip code A4530, Salta, Argentina. 5. PhD, Instituto de Investigaciones de Enfermedades Tropicales (IIET-CONICET), Universidad Nacional de Salta, Alvarado 751, San Ramon de la Nueva Oran, zip code A4530, Salta, Argentina, Cátedra de Química Biología, Facultad de Ciencias Naturales, Universidad Nacional de Salta, Av. Bolivia 5150, zip code A4400, Salta, Argentina. 6. MD, PhD, Instituto de Investigaciones de Enfermedades Tropicales (IIET-CONICET), Universidad Nacional de Salta, Alvarado 751, San Ramon de la Nueva Oran, zip code A4530, Salta, Argentina. 7. PhD, Instituto de Investigaciones en Energía no Convencional (INENCO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Salta, Av. Bolivia 5150, zip code A4400, Salta, Argentina, Simon A. Levin Mathematical, Computational and Modeling Sciences Center, Arizona State University, P.O. Box 873901, Tempe, AZ 85287-3901, USA.
Abstract
INTRODUCTION: The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. METHODS: An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms. RESULTS: Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever, odynophagia, and malaise. However, the presentation of these symptoms was different between the different age groups. In turn, at the time of presentation, the symptoms associated with respiratory problems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%. CONCLUSIONS: There were significant differences between the different age groups. Additionally, there were interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases. GERMS.
INTRODUCTION: The objective of this cross-sectional study was to describe the main symptoms associated with COVID-19, and their diagnostic characteristics, to aid in the clinical diagnosis. METHODS: An analysis of all patients diagnosed by RT-PCR for SARS-CoV-2 between April and May 2020 in Argentina was conducted. The data includes clinical and demographic information from all subjects at the time of presentation (n=67318, where 12% were positive for SARS-CoV-2). The study population was divided into four age groups: pediatric (0-17 years), young adults (18-44 years), adults (45-64 years), and elderly (65-103 years). Multivariate logistic regression was used to measure the association of all symptoms and to create a diagnostic model based on symptoms. RESULTS: Symptoms associated with COVID-19 were anosmia, dysgeusia, headache, low-grade fever, odynophagia, and malaise. However, the presentation of these symptoms was different between the different age groups. In turn, at the time of presentation, the symptoms associated with respiratory problems (chest pain, abdominal pain, and dyspnea) had a negative association with COVID-19 or did not present statistical relevance. On the other hand, the model based on 16 symptoms, age and sex, presented a sensitivity of 80% and a specificity of 46%. CONCLUSIONS: There were significant differences between the different age groups. Additionally, there were interactions between different symptoms that were highly associated with COVID-19. Finally, our findings showed that a regression model based on multiple factors (age, sex, interaction between symptoms) can be used as an accessory diagnostic method or a rapid screening of suspected COVID-19 cases. GERMS.
Authors: Jonathan J Deeks; Jacqueline Dinnes; Yemisi Takwoingi; Clare Davenport; René Spijker; Sian Taylor-Phillips; Ada Adriano; Sophie Beese; Janine Dretzke; Lavinia Ferrante di Ruffano; Isobel M Harris; Malcolm J Price; Sabine Dittrich; Devy Emperador; Lotty Hooft; Mariska Mg Leeflang; Ann Van den Bruel Journal: Cochrane Database Syst Rev Date: 2020-06-25