Clinical epidemiological applicability of real-time polymerase chain reaction for COVID-19
10.24171/j.phrp.2022.0135
- Author:
Geehyuk KIM
1
;
Jun-Kyu KANG
;
Jungho KIM
;
Jiyoung LEE
;
Jin GWACK
Author Information
1. Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea
- Publication Type:Original Article
- From:
Osong Public Health and Research Perspectives
2022;13(4):252-262
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:Real-time polymerase chain reaction is currently used as a confirmatory test for coronavirus disease 2019 (COVID-19). The test results are interpreted as positive, negative, or inconclusive, and are used only for a qualitative classification of patients. However, the test results can be quantitated using threshold count (Ct) values to determine the amount of virus present in the sample. Therefore, this study investigated the diagnostic usefulness of Ct results through various quantitative analyzes, along with an analysis of clinical and epidemiological characteristics.
Methods:Clinical and epidemiological data from 4,642 COVID-19 patients in April 2021 were analyzed, including the Ct values of the RNA-dependent RNA polymerase (RdRp), envelope (E), and nucleocapsid (N) genes. Clinical and epidemiological data (sex, age, underlying diseases, and early symptoms) were collected through a structured questionnaire. A correlation analysis was used to examine the relationships between variables.
Results:All 3 genes showed statistically significant relationships with symptoms and severity levels. The Ct values of the RdRp gene decreased as the severity of the patients increased. Moreover, statistical significance was observed for the presence of underlying diseases and dyspnea.
Conclusion:Ct values were found to be related to patients’ clinical and epidemiological characteristics. In particular, since these factors are closely related to symptoms and severity, Ct values can be used as primary data for predicting patients’ disease prognosis despite the limitations of this method. Conducting follow-up studies to validate this approach might enable using the data from this study to establish policies for preventing COVID-19 infection and spread.
