Prediction of severe infection based on genomic analysis of SARS-CoV-2

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A group of scientists have claimed that the severity of coronavirus disease 2019 (COVID-19) depends on the method of exposure, the pathogenicity of the causative agent, and the susceptibility of the host and its response to it. ‘pathogen.

To study: SARS-CoV-2 genome-based severity predictions correspond to lower qPCR values ​​and higher viral load. Image Credit: Adao / Shutterstock

However, the current COVID-19 pandemic, caused by the rapid outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has shown considerable diversity of both host and virus with a wide range of clinical outcomes.

Background

Previously, the severity of the disease was largely related to the phenotype of the host, e.g. gender, age, blood type, etc. This pandemic has shown that the geographic region, viral mutations and genetic susceptibility of the host have an important role in serious clinical outcomes.

To predict the severity of illness experienced by an individual, scientists used computer models based on phenotypic, genetic and demographic data.

The main objective of these models is to tailor the best treatment for a patient infected with SARS-CoV-2. Predicting the severity of the disease early can help preserve life and health.

Scientists said real-time PCR data with increased cycle thresholds could be linked to a 9% reduction in the risk of in-hospital mortality. They further found that patients with cycle thresholds below 23 were 3.9 times more likely to die in hospital than patients with cycle thresholds above 33.

Previous studies have found that while the use of PCR cycle thresholds is an effective predictor of COVID-19 outcome, it cannot distinguish between different levels of disease severity.

Scientists used genome-wide sequencing of SARS-CoV-2 to identify the newly emerged variants. Some of the variants have been classified as variants of concern, due to their greater virulence, transmissibility and vaccine evasion or the immune response induced by natural infection. These variants emerged due to a mutation in the spike sequence and other parts of the virus genome.

A group of researchers used this SARS-CoV-2 sequence data and developed an algorithm to predict severity based on viral mutations.

They also identified seventeen variants associated with severe clinical outcomes, and sixty-seven variants were associated with mild clinical symptoms. This report demonstrated the discriminatory ability to classify severe patients by performing an area under the curve analysis. Researchers recently focused on evaluating whether a genome-based predictive algorithm developed to predict clinical severity could also predict polymerase chain reaction (PCR) outcomes as a surrogate for determining viral load and gravity.

This study is available on the medRxiv* preprint server.

About the study

The current study has exhaustively validated the predictions of machine learning models and established its credibility through powerful analytical tools to predict disease severity. Scientists are confident that their findings would help clinicians determine a line of treatment tailored to a particular patient. The current study used an external group sample containing an orthogonal severity marker that supported the algorithm, which can identify strains of the virus that are biologically unique and have clinically significant differences.

A previous study associated with the Middle East Respiratory Syndrome Virus (MERS) reported that viral load is strongly linked to the possibility of serious infection and death. This study also indicated that a decrease in the cycle cut-off value increases the risk of mortality by 17%.

However, this study did not include factors such as the correlation between age, viral load, and disease severity. Currently, if a cycle cutoff value is less than 20, the individual is considered “highly infectious”.

The researchers used the previously published algorithm used to compare viral genome-based predictions of severity to PCR-based viral loads clinically derived from 716 viral genomes. Samples that represented severe COVID-19 results had an average cycle threshold of 18.3. Additionally, those with mild symptoms had an average cycle cutoff of 20.4. The present study projected a significant correlation between the predicted probability of severity and the cycle threshold.

Conclusion

This study had some limitations, including the use of the PCR cycle cutoff as a surrogate for clinical severity. However, the PCR test cannot fairly predict clinical outcome and is not an ideal measure.

Another limitation of the study was the small number of viral genotypes.

Despite these limitations, the study showed that a genome-based algorithm could be linked to metrics from clinical diagnostic tests, which could predict the severity of COVID-19.

Researchers have found that viral genetic information and patient demographics could help clinicians determine the appropriate COVID-19 treatment for an infected person.

In addition, SARS-CoV-2 sequence data as well as in silico-the derived severity markers could help design vaccines for new variants.

This study also said that genomic surveillance could help identify new viral strains with epidemic potential and, thus, give health officials enough time to prepare a strategy to contain transmission.

*Important Notice

medRxiv publishes preliminary scientific reports which are not peer reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or treated as established information.


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