A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil.

Autor: Diaz-Quijano FA; Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil.; Laboratório de Inferência Causal em Epidemiologia da Universidade de São Paulo, São Paulo, Brazil., da Silva JMN; Laboratório de Inferência Causal em Epidemiologia da Universidade de São Paulo, São Paulo, Brazil.; Postgraduate Program in Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil., Ganem F; Department of Immunization and Communicable Diseases, Secretariat of Health Surveillance, Ministry of Health, Brasília, Brazil., Oliveira S; Department of Immunization and Communicable Diseases, Secretariat of Health Surveillance, Ministry of Health, Brasília, Brazil., Vesga-Varela AL; Laboratório de Inferência Causal em Epidemiologia da Universidade de São Paulo, São Paulo, Brazil.; Postgraduate Program in Public Health, School of Public Health, University of São Paulo, São Paulo, Brazil., Croda J; School of Medicine, Federal University of Mato Grosso do Sul, Campo Grande, Brazil.; Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, USA.; Oswaldo Cruz Foundation, Mato Grosso do Sul, Campo Grande, Brazil.
Jazyk: angličtina
Zdroj: Tropical medicine & international health : TM & IH [Trop Med Int Health] 2020 Nov; Vol. 25 (11), pp. 1385-1394. Date of Electronic Publication: 2020 Sep 07.
DOI: 10.1111/tmi.13476
Abstrakt: Objective: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system.
Methods: We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation.
Results: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41-96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51-97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%).
Conclusion: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends.
(© 2020 John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz: