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The amount of SARS-CoV-2 RNA in the blood predicts the risk of death

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Despite advancements in Covid-19 care, it remains difficult to identify patients who are at a higher risk of dying from the condition and offer them new medications.

According to a study published in “Science Advances,” the amount of SARS-CoV-2 genetic material (viral RNA) in the blood is a reliable marker of which individuals would die from Covid-19.

The study was able to discover which biomarkers are predictors of mortality within 60 days following the onset of symptoms, according to the researchers.

“Thanks to our data, we have successfully developed and validated a statistical model based on one blood biomarker, viral RNA,” said Daniel Kaufmann, from the University of Montreal (Canada).

Despite advancements in COVID-19 care, doctors have had difficulty identifying patients who are most at risk of dying from the condition and thus being able to offer them new medications.

Although various biomarkers have been established, physicians’ ability to make timely medical choices is hampered by the abundance of parameters that cannot be juggled in a clinical context.

Kaufmann’s team examined the levels of inflammatory proteins in blood samples acquired from 279 patients during their hospitalization with Covid-19, ranging in severity from moderate to critical, seeking for any that stood out.

In addition, two additional authors of the study, Nicolas Chomont and Andrés Finzi, assessed the amount of viral RNA and the levels of antibodies that target the virus. Patients were observed for a minimum of 60 days after receiving samples 11 days following the onset of symptoms.

The researchers wanted to see if immunological markers were linked to an increased risk of death.

“Among all of the biomarkers we evaluated, we showed that the amount of viral RNA in the blood was directly associated with mortality and provided the best predictive response, once our model was adjusted for the age and sex of the patient,” explained Elsa Brunet-Ratnasingham, a doctoral student in Kaufmann’s lab and co-first author of the study.

“We even found that including additional biomarkers did not improve predictive quality.”

Kaufmann and Brunet-Ratnasingham investigated the model in two different cohorts of infected individuals collected during the first, second, and third waves of the pandemic to confirm its efficacy.

The predicted model worked in every case, regardless of which hospital the patients were treated in or what stage of the epidemic they were in.

“It would be interesting to use the model to monitor patients,” he condcluded, “with the following question in mind: when you administer new treatments that have proven effective, is viral load still a predictive marker of mortality?”

Source: DOI: 10.1126/sciadv.abj5629.

Image Credit: shutterstock

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