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Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends

ABSTRACT: Introduction: We assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemic's dynamics at local and national levels and for improving forecasting models. Methods: SARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium. Results: Over 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak, and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes. Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic's trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.

SUBMITTER: Yin N

PROVIDER: S-EPMC8591051 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. The utility of cycle threshold, at admission, as a predictor of disease severity has not been thoroughly investigated.Methods and findingsWe conducted a retrospective study of SARS-CoV-2 positive, hospitalized patients from 3/26/2020 to 8/5/2020 who had SARS-CoV-2 Ct data within 48 hours of admission (n = 1044). Only patients with complete survival data, discharged (n = 774) or died in hospital (n = 270), were included in our analysis. Laboratory, demographic, and clinical data were extracted from electronic medical records. Multivariable logistic regression was applied to examine the relationship of patient mortality with Ct values while adjusting for established risk factors. Ct was analyzed as continuous variable and subdivided into quartiles to better illustrate its relationship with outcome. Cumulative incidence curves were created to assess whether there was a survival difference in the setting of the competing risks of death versus patient discharge. Mean Ct at admission was higher for survivors (28.6, SD = 5.8) compared to non-survivors (24.8, SD = 6.0, P<0.001). In-hospital mortality significantly differed (p<0.05) by Ct quartile. After adjusting for age, gender, BMI, hypertension and diabetes, increased cycle threshold was associated with decreased odds of in-hospital mortality (0.91, CI 0.89-0.94, p<0.001). Compared to the 4th Quartile, patients with Ct values in the 1st Quartile (Ct <22.9) and 2nd Quartile (Ct 23.0-27.3) had an adjusted odds ratio of in-hospital mortality of 3.8 and 2.6 respectively (p<0.001). The discriminative ability of Ct to predict inpatient mortality was found to be limited, possessing an area under the curve (AUC) of 0.68 (CI 0.63-0.71).ConclusionSARS-CoV-2 Ct was found to be an independent predictor of patient mortality. However, further study is needed on how to best clinically utilize such information given the result variation due to specimen quality, phase of disease, and the limited discriminative ability of the test.

Project description:Coronavirus disease 2019 (COVID-19) is an infectious, acute respiratory disease caused mainly by person-to-person transmission of the coronavirus SARS-CoV-2. Its emergence has caused a world-wide acute health crisis, intensified by the challenge of reliably identifying individuals likely to transmit the disease. Diagnosis is hampered by the many unknowns surrounding this disease, including those relating to infectious viral burden. This uncertainty is exacerbated by disagreement surrounding the clinical relevance of molecular testing using reverse transcription quantitative PCR (RT-qPCR) for the presence of viral RNA, most often based on the reporting of quantification cycles (Cq), which is also termed the cycle threshold (Ct) or crossing point (Cp). Despite it being common knowledge that Cqs are relative values varying according to a wide range of different parameters, there have been efforts to use them as though they were absolute units, with Cqs below an arbitrarily determined value, deemed to signify a positive result and those above, a negative one. Our results investigated the effects of a range of common variables on Cq values. These data include a detailed analysis of the effect of different carrier molecules on RNA extraction. The impact of sample matrix of buccal swabs and saliva on RNA extraction efficiency was demonstrated in RT-qPCR and the impact of potentially inhibiting compounds in urine along with bile salts were investigated in RT-digital PCR (RT-dPCR). The latter studies were performed such that the impact on the RT step could be separated from the PCR step. In this way, the RT was shown to be more susceptible to inhibitors than the PCR. Together, these studies demonstrate that the consequent variability of test results makes subjective Cq cut-off values unsuitable for the identification of infectious individuals. We also discuss the importance of using reliable control materials for accurate quantification and highlight the substantial role played by dPCR as a method for their development.

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Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends

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