Clinical diagnosis of COVID-19. A multivariate logistic regression analysis of symptoms of COVID-19 at presentation

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 thedifferent age groups. In turn, at the time of presentation, the symptoms associated with respiratoryproblems (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, therewere 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.