How to Identify COVID-19 Infected Patients at the Risk of Dying?

The amount of viral RNA, the COVID-19 genetic material in the blood will be a reliable indicator to detect patients who will be dying from the disease, according to a team led by Université de Montréal medical professor Dr. Daniel Kaufmann.

The finding is published today in Science Advances.

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‘The amount of viral RNA in the blood was directly associated with mortality.’

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“In our study, we were able to determine which biomarkers are predictors of mortality in the 60 days following the onset of symptoms,” said Kaufmann, the study’s co-lead author alongside CRCHUM research colleagues Nicolas Chomont and Andrés Finzi.

Despite advances in the management of COVID-19, doctors have found it hard to identify patients most at risk of dying of the disease and so be able to offer them new treatments.

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

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Using blood samples collected from 279 patients during their hospitalization for COVID-19, ranging in degrees of severity from moderate to critical, Kaufmann’s team measured amounts of inflammatory proteins, looking for any that stood out.

At the same time, they measured the amounts of viral RNA and 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.

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Among all of the biomarkers evaluated, the amount of viral RNA in the blood was directly associated with mortality and provided the best predictive response.

To confirm its effectiveness, they tested it on two independent cohorts of infected patients from Montreal’s Jewish General Hospital (recruited during the first wave of the pandemic) and the CHUM (recruited during the second and third waves).

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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 research team want to put it to practical use.

Source-Medindia