Epidemiological patterns of syndromic symptoms in suspected patients with COVID-19 in Iran: A Latent Class Analysis

Ali Hosseinzadeh, Maysam Rezapour, Marzie Rohani-Rasaf, Mohammad Hassan Emamian, Solmaz Talebi, Shahrbanoo Goli, Reza Chaman, Hossein Sheibani, Ehsan Binesh, Fariba Zare, Ahmad Khosravi

Abstract


Background: Early diagnosis and supportive treatments are essential to patients with coronavirus disease 2019 (COVID-19). Therefore, the current study aimed to determine different patterns of syndromic symptoms and sensitivity and specificity of each of them in the diagnosis of COVID-19 in suspected patients.

Study Design: Cross-sectional study

Methods: In this study, the retrospective data of 1,539 patients suspected of COVID-19 were obtained from a local registry under the supervision of the officials at Shahroud University of Medical Sciences, Shahroud, Iran. A Latent Class Analysis (LCA) was carried out on syndromic symptoms, and the associations of some risk factors and latent subclasses were accessed using one-way analysis of variance and Chi-square test.

Results: The LCA indicated that there were three distinct subclasses of syndromic symptoms among the COVID-19 suspected patients. The age, former smoking status, and body mass index were associated with the categorization of individuals into different subclasses. In addition, the sensitivity and specificity of class 2 (labeled as “High probability of polymerase chain reaction [PCR]+”) in the diagnosis of COVID-19 were 67.43% and 76.17%, respectively. Furthermore, the sensitivity and specificity of class 3 (labeled as “Moderate probability of PCR+”) in the diagnosis of COVID-19 were 75.92% and 50.23%, respectively.

Conclusions: The findings of the present study showed that syndromic symptoms, such as dry cough, dyspnea, myalgia, fatigue, and anorexia, might be helpful in the diagnosis of suspected COVID-19 patients.

 


Keywords


COVID-19; Latent Class Analysis; Epidemiological pattern; Diagnosis

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