Chronological changes of viral shedding in adult inpatients with Omicron infection in Shanghai, China.

Background: Coronavirus disease 2019 (COVID-19) caused by the Omicron variant occurred in Shanghai, China, but its clinical characteristics and virology have not been comprehensively described. Methods: This retrospective cohort study included adult inpatients (≥18 years) diagnosed with COVID-19 at Changhai Hospital. Laboratory and clinical data were obtained from electronic medical records to investigate the clinical characteristics of COVID-19 and the variations in the patients' laboratory indexes were examined. Results: The symptoms of COVID-19 caused by the Omicron variant were relatively mild. Upper respiratory tract specimens yielded higher positive detection rates than lower respiratory tract and intestinal specimens. Peak COVID-19 viral load was reached at the time of admission; quantification cycle (Cq) values increased to approximately 35 after 8.54 days. In vivo viral shedding duration correlated with age and disease severity (p<0.05). The older the patient and the more severe the disease, the longer the duration of viral shedding was. Portion parameters of blood routine, coagulative function, clinical chemistry, and inflammatory factor showed a certain correlation with the SARS-CoV-2 viral load. Conclusions: Virus replication and shedding are rapid in Omicron-positive patients; COVID-19 in these patients is characterized by acute onset, mild symptoms, and fast recovery. Older patients and those with more severe disease demonstrate prolonged virus shedding. Routine hematological indexes can reveal disease severity and help clinically evaluate the patient's condition.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology.

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

The Dangers of Using Cq to Quantify Nucleic Acid in Biological Samples: A Lesson From COVID-19.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA quantities, measured by reverse transcription quantitative PCR (RT-qPCR), have been proposed to stratify clinical risk or determine analytical performance targets. We investigated reproducibility and how setting diagnostic cutoffs altered the clinical sensitivity of coronavirus disease 2019 (COVID-19) testing. METHODS: Quantitative SARS-CoV-2 RNA distributions [quantification cycle (Cq) and copies/mL] from more than 6000 patients from 3 clinical laboratories in United Kingdom, Belgium, and the Republic of Korea were analyzed. Impact of Cq cutoffs on clinical sensitivity was assessed. The June/July 2020 INSTAND external quality assessment scheme SARS-CoV-2 materials were used to estimate laboratory reported copies/mL and to estimate the variation in copies/mL for a given Cq. RESULTS: When the WHO-suggested Cq cutoff of 25 was applied, the clinical sensitivity dropped to about 16%. Clinical sensitivity also dropped to about 27% when a simulated limit of detection of 106 copies/mL was applied. The interlaboratory variation for a given Cq value was >1000 fold in copies/mL (99% CI). CONCLUSION: While RT-qPCR has been instrumental in the response to COVID-19, we recommend Cq (cycle threshold or crossing point) values not be used to set clinical cutoffs or diagnostic performance targets due to poor interlaboratory reproducibility; calibrated copy-based units (used elsewhere in virology) offer more reproducible alternatives. We also report a phenomenon where diagnostic performance may change relative to the effective reproduction number. Our findings indicate that the disparities between patient populations across time are an important consideration when evaluating or deploying diagnostic tests. This is especially relevant to the emergency situation of an evolving pandemic.

COVID-19 and Diagnostic Testing for SARS-CoV-2 by RT-qPCR-Facts and Fallacies.

Although molecular testing, and RT-qPCR in particular, has been an indispensable component in the scientific armoury targeting SARS-CoV-2, there are numerous falsehoods, misconceptions, assumptions and exaggerated expectations with regards to capability, performance and usefulness of the technology. It is essential that the true strengths and limitations, although publicised for at least twenty years, are restated in the context of the current COVID-19 epidemic. The main objective of this commentary is to address and help stop the unfounded and debilitating speculation surrounding its use.