A statistical model developed by researchers uses a blood biomarker of SARS-CoV-2 to identify infected patients who are most at risk of dying of COVID-19.

The study by researchers at the Université de Montréal (Montréal, Canada) 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. 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.

Using blood samples collected from 279 patients during their hospitalization for COVID-19, ranging in degrees of severity from moderate to critical, the research team measured amounts of inflammatory proteins, looking for any that stood out. At the same time, the researchers also 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. The goal was to test the hypothesis that immunological indicators were associated with increased mortality.

“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,” said Elsa Brunet-Ratnasingham, a doctoral student and co-first author of the study. “We even found that including additional biomarkers did not improve predictive quality.”

“In our study, we were able to determine which biomarkers are predictors of mortality in the 60 days following the onset of symptoms,” said Dr. Daniel Kaufmann, Université de Montréal medical professor who led the research team. “Thanks to our data, we have successfully developed and validated a statistical model based on one blood biomarker,” viral RNA. 

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