Abstract

Simple Summary COVID-19 is frequently suspected based on clinical features, such as fever, cough, headache, or loss of taste. However, it remains unclear whether these manifestations are reliable indicators of disease. We sought to evaluate the diagnostic accuracy of clinical manifestations in identifying patients with COVID-19. Data from a nationwide database comprising of 2,187,962 patients who sought medical care in Chile were analysed. Information regarding age, gender, type of insurance, a history of a close contact with COVID-19, and several clinical features was obtained. The most common complaints were headache, muscle aches, and cough. No single clinical feature was precise enough to fully confirm or exclude COVID-19. The combination of several of these manifestations with epidemiological risk factors into a model showed a reasonable accuracy in detecting cases of COVID-19. (1) Background: The diagnosis of COVID-19 is frequently made on the basis of a suggestive clinical history and the detection of SARS-CoV-2 RNA in respiratory secretions. However, the diagnostic accuracy of clinical features is unknown. (2) Objective: To assess the diagnostic accuracy of patient-reported clinical manifestations to identify cases of COVID-19. (3) Methodology: Cross-sectional study using data from a national registry in Chile. Infection by SARS-CoV-2 was confirmed using RT-PCR in all cases. Anonymised information regarding demographic characteristics and clinical features were assessed using sensitivity, specificity, and diagnostic odds ratios. A multivariable logistic regression model was constructed to combine epidemiological risk factors and clinical features. (4) Results: A total of 2,187,962 observations were available for analyses. Male participants had a mean age of 43.1 +/- 17.5 years. The most common complaints within the study were headache (39%), myalgia (32.7%), cough (31.6%), and sore throat (25.7%). The most sensitive features of disease were headache, myalgia, and cough, and the most specific were anosmia and dysgeusia/ageusia. A multivariable model showed a fair diagnostic accuracy, with a ROC AUC of 0.744 (95% CI 0.743-0.746). (5) Discussion: No single clinical feature was able to fully confirm or exclude an infection by SARS-CoV-2. The combination of several demographic and clinical factors had a fair diagnostic accuracy in identifying patients with the disease. This model can help clinicians tailor the probability of COVID-19 and select diagnostic tests appropriate to their setting.