Dataset Information

SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases.

ABSTRACT:

Aim

Anticipating local surges in COVID-19 cases has predominantly been based on observation of increasing cases. We sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values from clinical testing were predictive of future cases.

Methods

Data were collected from a large, safety-net hospital in Los Angeles, California. Ct values for all SARS-CoV-2 detections by the GeneXpert system (Cepheid) between October 2020 to March 2021 were analyzed.

Results

A total of 2,114 SARS-CoV-2-positive samples were included. Cases increased dramatically in December 2020, peaking the first week of January, before returning to pre-surge numbers by mid-February. Ct values fell during this same period, with values in December and January (25.6 ± 7.8 and 27±7.9, respectively) significantly lower than those of the other months (30±9.3 to 37.7 ± 6.3). Average weekly Ct values for all patients negatively correlated with the number of tests run two weeks in the future (r= -0.74, p<0.0001), whereas Ct values for asymptomatic patients negatively correlated most strongly with total number of tests performed one month later (r= -0.88, p<0.0001). Predictive modeling using these Ct values correctly predicted whether cases would increase or decrease 65% of the time for a subsequent surge (May-July 2021).

Conclusions

During the largest COVID-19 surge in Los Angeles to date, we observed significantly lower Ct values (representing higher levels of viral RNA) suggesting that increased transmission of COVID-19 was temporarily associated with higher viral loads. Decreasing Ct values appear to be a leading indicator for predicting future COVID-19 cases, which can facilitate improved hospital-level surge planning.

SUBMITTER: Phillips MC

PROVIDER: S-EPMC8990441 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases.

Phillips Matthew C MC Quintero David D Wald-Dickler Noah N Holtom Paul P Butler-Wu Susan M SM

Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology 20220408

<h4>Aim</h4>Anticipating local surges in COVID-19 cases has predominantly been based on observation of increasing cases. We sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values from clinical testing were predictive of future cases.<h4>Methods</h4>Data were collected from a large, safety-net hospital in Los Angeles, California. Ct values for all SARS-CoV-2 detections by the GeneXpert system (Cepheid) between October 2020 to March 2021 were analyzed.<h4>Results</h4>A to ...[more]

PMID: 35472751

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Project description:BackgroundThe distribution of population-level real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values as a proxy of viral load may be a useful indicator for predicting COVID-19 dynamics.ObjectiveThe aim of this study was to determine the relationship between the daily trend of average Ct values and COVID-19 dynamics, calculated as the daily number of hospitalized patients with COVID-19, daily number of new positive tests, daily number of COVID-19 deaths, and number of hospitalized patients with COVID-19 by age. We further sought to determine the lag between these data series.MethodsThe samples included in this study were collected from March 21, 2021, to December 1, 2021. Daily Ct values of all patients who were referred to the Molecular Diagnostic Laboratory of Iran University of Medical Sciences in Tehran, Iran, for RT-PCR tests were recorded. The daily number of positive tests and the number of hospitalized patients by age group were extracted from the COVID-19 patient information registration system in Tehran province, Iran. An autoregressive integrated moving average (ARIMA) model was constructed for the time series of variables. Cross-correlation analysis was then performed to determine the best lag and correlations between the average daily Ct value and other COVID-19 dynamics-related variables. Finally, the best-selected lag of Ct identified through cross-correlation was incorporated as a covariate into the autoregressive integrated moving average with exogenous variables (ARIMAX) model to calculate the coefficients.ResultsDaily average Ct values showed a significant negative correlation (23-day time delay) with the daily number of newly hospitalized patients (P=.02), 30-day time delay with the daily number of new positive tests (P=.02), and daily number of COVID-19 deaths (P=.02). The daily average Ct value with a 30-day delay could impact the daily number of positive tests for COVID-19 (β=-16.87, P<.001) and the daily number of deaths from COVID-19 (β=-1.52, P=.03). There was a significant association between Ct lag (23 days) and the number of COVID-19 hospitalizations (β=-24.12, P=.005). Cross-correlation analysis showed significant time delays in the average Ct values and daily hospitalized patients between 18-59 years (23-day time delay, P=.02) and in patients over 60 years old (23-day time delay, P<.001). No statistically significant relation was detected in the number of daily hospitalized patients under 5 years old (9-day time delay, P=.27) and aged 5-17 years (13-day time delay, P=.39).ConclusionsIt is important for surveillance of COVID-19 to find a good indicator that can predict epidemic surges in the community. Our results suggest that the average daily Ct value with a 30-day delay can predict increases in the number of positive confirmed COVID-19 cases, which may be a useful indicator for the health system.

Dataset Information

SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases.

Aim

Methods

Results

Conclusions

Publications

SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases.

Similar Datasets

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