The Evaluation of Diagnostic Values of Clinical Symptoms for COVID-19 Hospitalized Patients in Northern Iran: The Syndromic Surveillance System Data

Background: A novel coronavirus led to a rapidly spreading outbreak of COVID-19, which caused morbidity and mortality worldwide. Appropriate case definitions can help diagnose COVID-19. Objectives: This study aimed evaluate the COVID-19 clinical symptoms their potential patterns using latent class analysis (LCA) for identifying confirmed cases among hospitalized patients in northern Iran according syndromic surveillance system data. Methods: cross-sectional was conducted on with admitted hospitals Mazandaran Province, Iran. Respiratory specimens were collected by nasopharyngeal swabs from tested COVID-¬19 reverse transcription polymerase chain reaction (RT-PCR). Latent used identify symptoms. The sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC) each symptom pattern compared plotted. Also, multiple logistic regression determine odds ratio predicting infection adjusting gender age groups. Results: Among 13,724 included analyses, 4,836 (35, 2%) had RT-PCR fever, chills, cough, shortness breath, fatigue, myalgia, sore throat, diarrhea, nausea or vomiting, headache, arthralgia significantly more common positive than other LCA. suggested six classes (patterns) AUC poor, being 0.43 5, comprising without any symptoms, 0.53 3, cough. showed that 1, arthralgia, an 2.87 (1.39, 3.43) relative 5 (patients symptoms) Conclusions: might However, defined during this time not appropriate cases.