Epidemiological patterns of syndromic symptoms in suspected patients with COVID-19 in Iran: A Latent Class Analysis
- PMID: 34024766
- PMCID: PMC8957697
- DOI: 10.34172/jrhs.2021.41
Epidemiological patterns of syndromic symptoms in suspected patients with COVID-19 in Iran: A Latent Class Analysis
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.
Conclusion: 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; Diagnosis; Epidemiological pattern; Latent Class Analysis.
References
-
- Hopkins C, Kumar N. Loss of sense of smell as marker of COVID-19 infection. ENTUK Web Site; 2020 [updated 4 June 2020; cited 30 Oct 2020]; Available from: https://www.entuk.org/sites/default/files/files/Loss%20of%20sense%20of%2....
-
- COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Johns Hopkins University of Medicine: Coronavirus Resource Center Web Site; 2020 [updated 8 Jan 2021; cited 20 May 2020]; Available from: https://coronavirus.jhu.edu/map.html.
MeSH terms
LinkOut - more resources
Full Text Sources
Medical
Research Materials