Association between RT-PCR Ct Values and COVID-19 New Daily Cases: A Multicenter Cross-Sectional Study

  1. Abdulkarim Abdulrahman
  2. Saad Mallah
  3. Abdulla Ismael AlAwadhi
  4. Simone Perna
  5. Essam Janahi
  6. Manaf AlQahtani

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Introduction: Proactive prediction of the epidemiologic dynamics of viral diseases and outbreaks of the likes of COVID-19 has remained a difficult pursuit for scientists, public health researchers, and policymakers. It is unclear whether RT-PCR Cycle Threshold (Ct) values of COVID-19 (or any other virus) as indicator of viral load, could represent a possible predictor for underlying epidemiological changes on a population level. Objectives: To investigate whether population-wide changes in SARS-CoV-2 RT-PCR Ct values over time are associated with the daily fraction of positive COVID-19 tests. In addition, this study analyses the factors that could influence the RT-PCR Ct values. Method: A retrospective cross-sectional study was conducted on 63,879 patients from May 4, 2020 to September 30, 2020, in all COVID-19 facilities in the Kingdom of Bahrain. Data collected included number of tests and newly diagnosed cases, as well as Ct values, age, gender nationality, and symptomatic status. Results: Ct values were found to be negatively and very weakly correlated with the fraction of daily positive cases in the population r = -0.06 (CI95%: -0.06; -0.05; p=0.001). The R-squared for the regression model (adjusting for age and number of daily tests) showed an accuracy of 45.3%. Ct Values showed an association with nationality (p=0.012). After the stratification, the association between Ct values and the fraction of daily positive cases was only maintained for the female gender and Bahraini-nationality. Symptomatic presentation was significantly associated with lower Ct values (higher viral loads). Ct values do not show any correlation with age (p=0.333) or gender (p=0.522). Conclusion: We report one of the first and largest studies to investigate the epidemiological associations of Ct values with COVID-19. Ct values offer a potentially simple and widely accessible tool to predict and model epidemiological dynamics on a population level. More population studies and predictive models from global cohorts are necessary.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.12.07.20245233: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite this being one of the largest cohorts on COVID-19 in the region, and the largest to investigate Ct values in relation to case trajectory, the limitations are important to be addressed. To begin with, although this is a multi-center analysis, it remains a national one in a small country. National public health measures and screening policies differ from one place to the other, which necessitates the need for a global cohort when investigating the macro-behaviors and characteristics of COVID-19. The Ct values as reported in our study have been fairly stable throughout the five-month period from a statistical standpoint, which would make it non-ideal to test for associations between Ct values and case trajectory. For that reason, longer periods or cohorts from different populations may be more suitable to investigate an association. Screening policies additionally play a vital role in determining the cohort characteristics. Our study involved cases from varying screening approaches, including admitted patients, symptomatic suspected cases in outpatient setting, contact tracing, population screening in case of travel, random testing in the community and individual-requested testing. In order to minimize bias and confounding factors, ideally only cohorts from randomized population screening should be included. This would also help control the number of tests conducted per day. The different frequencies of testing may have impacted the proportion of positive cases identifie...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.12.07.20245233v1 on medRxiv