Scientists have developed a new way of identifying Covid patients most likely to die - here's how:

An accurate way of identifying which Covid patients are most likely to die from the virus has been developed.

Friday, 26th November 2021, 12:12 pm

The statistical model developed by Canadian researchers uses a blood biomarker of SARS-CoV-2 to identify infected patients most at risk.

The team found that the amount of a SARS-CoV-2 genetic material - viral RNA - in the blood is a "reliable indicator" in detecting which patients will die of the disease.

Study leader Professor Daniel Kaufmann, of the University of Montreal, said: “We were able to determine which biomarkers are predictors of mortality in the 60 days following the onset of symptoms.

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An accurate way of identifying which Covid patients are most likely to die from the virus has been developed.

“Thanks to our data, we have successfully developed and validated a statistical model based on one blood biomarker."

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Several biomarkers have been identified in other studies, but doctors say that juggling the profusion of parameters is not possible in a clinical setting and hinders their ability to make quick medical decisions.

Using blood samples collected from 279 patients while hospitalised with Covid - ranging in degrees of severity from "moderate" to "critical" - Prof Kaufmann’s team measured amounts of inflammatory proteins, looking for any that stood out.

At the same time, one team measured the amounts of viral RNA while another group recorded the levels of antibodies targeting the virus.

Samples were collected 11 days after the onset of symptoms and patients were monitored for a minimum of 60 days after that.

The goal was to test the hypothesis that immunological indicators were associated with increased mortality.

Study co first author Elsa Brunet-Ratnasingham, a doctoral student in Prof Kaufmann’s lab, said: “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.

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

To confirm its effectiveness, the team tested the model on two independent groups of infected patients from different hospitals and different periods of the pandemic.

It made no difference which hospital the patients were treated at, nor which period of the pandemic they fell into: in all cases, the predictive model worked.

Now Prof Kaufmann and his colleagues want to put it to practical use.

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

The findings were published in the journal Science Advances.

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