Persistence of live virus in critically ill patients infected with SARS-COV-2: a prospective observational study

Background Research on the duration of infectivity of ICU patients with COVID-19 has been sparse. Tests based on Reverse Transcriptase polymerase chain reaction (RT-PCR) detect both live virus and non-infectious viral RNA. We aimed to determine the duration of infectiousness based on viral culture of nasopharyngeal samples of patients with COVID-19. Methods Prospective observational study in adult intensive care units with a diagnosis of COVID-19 Pneumonia. Patients had repeated nasopharyngeal sampling performed after day 10 of ICU admission. Culture positive rate (based on viral culture on Vero cells in a level 4 lab) and Cycle threshold from RT-PCR were measured. Results Nine patients of the 108 samples (8.3%, 95% CI 3.9–15.2%) grew live virus at a median of 13 days (interquartile range 11–19) after their initial positive test. 74.1% of patients were RT-PCR positive but culture negative, and the remaining (17.6%) were RT-PCR and culture negative. Cycle threshold showed excellent ability to predict the presence of live virus, with a Ct < 25 with an AUC of 0.90 (95% CI 0.83–0.97, p < 0.001). The specificity of a Ct > 25 to predict negative viral culture was 100% (95% CI 70–100%). Conclusion 8.3% of our ICU patients with COVID-19 grew live virus at a median of 13 days post-initial positive RT-PCR test. Severity of illness, use of mechanical ventilation, and time between tests did not predict the presence of live virus. Cycle threshold of > 25 had the best ability to determine the lack of live virus in these patents.

From Delta to Omicron: analysing the SARS-CoV-2 epidemic in France using variant-specific screening tests (September 1 to December 18, 2021)

We analysed 131,478 SARS-CoV-2 variant screening tests performed in France from September 1st to December 18, 2021. Tests consistent with the presence of the Omicron variant exhibit significantly higher cycle threshold Ct values, which could indicate lower amounts of virus genetic material. We estimate that the transmission advantage of the Omicron variant over the Delta variant is +105% (95% confidence interval: 96-114%). Based on these data, we use mechanistic mathematical modelling to explore scenarios for early 2022.

Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies

Objectives To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL. Methods A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy. Results The model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance. Conclusion The modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.

Reinfection with SARS-CoV-2: outcome, risk factors and vaccine efficacy in a Scottish cohort

Background -- The objective of this study was to investigate how protection against COVID-19 conferred by previous infection is modified by vaccination. Methods -- In a cohort of all 152655 individuals in Scotland alive at 90 days after a positive test for SARS-CoV-2 (confirmed by cycle threshold < 30, or two tests) followed till 22 September 2021, rate ratios for reinfection were estimated with calendar time or tests as timescale. Findings -- Rates of detected and hospitalised reinfection with COVID-19 while unvaccinated were respectively 6.8 (95% CI 6.4 to 7.2) and 0.18 (95% CI 0.12 to 0.25) per 1000 person-months. These rates were respectively 68% and 74% lower than in a matched cohort of individuals who had not previously tested positive. Efficacy of two doses of vaccine in those with previous infection was estimated as as 84% (95 percent CI 81% to 86%) against detected reinfection and 71% (95 percent CI 29% to 88%) against hospitalised or fatal reinfection. The rate of detected reinfection after two doses of vaccine was 1.35 (95% CI 1.02 to 1.78) times higher in those vaccinated before first infection than in those unvaccinated at first infection. Interpretation -- The combination of natural infection and vaccination provides maximal protection against new infection with SARS-CoV-2: prior vaccination does not impair this protection.

SARS-CoV-2 genome-based severity predictions correspond to lower qPCR values and higher viral load

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. In order to reduce the impact on human life, it is critical to rapidly identify which genetic variants result in increased virulence or transmission. To address the former, we evaluated if a genome-based predictive algorithm designed to predict clinical severity could predict polymerase chain reaction (PCR) results, as a surrogate for viral load and severity. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically-derived PCR-based viral load of 716 viral genomes. For those samples predicted to be severe (predicted severity score > 0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the mild category (severity prediction < 0.5) had an average Ct of 20.4 (P = 0.0017). We found a non-trivial correlation between predicted severity probability and cycle threshold (r = -0.199). Additionally, when divided into quartiles by prediction severity probability, the most probable quartile (≥75% probability) had a Ct of 16.6 (n=10) as compared to those least probable to be severe (<25%) of 21.4 (n=350) (P = 0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to the metrics from the clinical diagnostic test, and that relative severity may be inferred from diagnostic values.

Lower SARS-CoV-2 viral shedding following COVID-19 vaccination among healthcare workers in Los Angeles, California

Among 880 healthcare workers with a positive SARS-CoV-2 test, 264 (30.0%) infections were identified following receipt of at least one vaccine dose. Median SARS-CoV-2 cycle threshold values were highest among individuals receiving two vaccine doses, corresponding to lower viral shedding. Vaccination might lead to lower transmissibility of SARS-CoV-2.

Sampling site for SARS-CoV-2 RT-PCR—An intrapatient four-site comparison from Tampere, Finland

Background SARS-CoV-2 diagnosis relies on the performance of nasopharyngeal swabs. Alternative sample sites have been assessed but the heterogeneity of the studies have made comparing different sites difficult. Objectives Our aim was to compare the performance of four different sampling sites for SARS-CoV-2 samples with nasopharynx being the benchmark. Study design COVID-19 positive patients were recruited prospectively, and samples were collected and analysed for SARS-CoV-2 with RT-PCR from all four anatomical sites in 43 patients, who provided written informed consent. Results All anterior nasal and saliva samples were positive, while two oropharyngeal samples were negative. There was no significant difference in the cycle threshold values of nasopharyngeal and anterior nasal samples while saliva and oropharynx had higher cycle threshold values. Conclusions Anterior nasal swab performs as good as nasopharynx swab with saliva also finding all the positives but with higher cycle threshold values. Thus, we can conclude that anterior nasal swabs can be used for SARS-CoV-2 detection instead of nasopharyngeal swabs if the situation would require so.

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

Background The threat of surging COVID-19 cases prompted many hospitals in the United States to preemptively suspend elective procedures throughout the pandemic. Utilizing samples from a large hospital in Los Angeles, we sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values (proxy for viral RNA loads) were predictive for the number of future COVID-19 cases. Methods Nasopharyngeal specimens on symptomatic patients and asymptomatic admissions were tested using the Xpert Xpress SARS-CoV-2 and SARS-CoV-2/Flu/RSV assays (Cepheid). Ct values for all SARS-CoV-2 detections between October 2020 to March 2021 were compiled for analysis. Results A total of 2,114 SARS-CoV-2-positive samples were included. The number of tests performed per week increased dramatically in December 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 were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P&lt; 0.001) and two weeks later (R= -0.75, P&lt; 0.0001). Ct values for patients who were asymptomatic at the time of testing most strongly correlated with total number of tests performed one month later (R= -0.86, P&lt; 0.0001). Average weekly Ct values and number of test run As cases (light grey) increased during December and January, there was a significant decrease in Ct values (dark grey) during that same time period. Average Ct values are a leading indicator of cases Average weekly Ct values for all patients (light grey) were significantly, negatively correlated with the number of tests run the following week (R= -0.71, P&lt;0.001) and two weeks later (R= -0.75, P&lt;0.0001). Ct values for patients who were asymptomatic at the time of testing (dark grey) most strongly correlated with total number of tests performed one month later (R= -0.86, P&lt;0.0001). Conclusion Lower Ct values, representing higher levels of viral RNA, have been associated with risk of intubation and infectivity. During the winter surge, we observed significantly lower Ct values suggesting that the increased transmission and morbidity of COVID-19 was temporarily associated with higher viral loads. Interestingly, Ct values for asymptomatic patients were most strongly associated with number of cases observed 1 months in the future, suggesting that asymptomatic viral load may be a leading indicator for forthcoming outbreaks. Given this association, Ct values may be a useful tool for predicting regional outbreaks of COVID-19 and more judicious cessation of elective procedures. Disclosures All Authors: No reported disclosures