A statistical approach created by researchers at the Université de Montréal uses a SARS-CoV-2 blood biomarker to identify infected patients who are most likely to die from COVID-19.

New research led by Université de Montréal medical professor Dr. Daniel Kaufmann discovered that the amount of SARS-CoV-2 genetic material—viral RNA—in the blood is a better predictor in identifying which patients will die of the disease.

The research was carried out by Kaufmann and his colleagues at the CRCHUM, which is the research arm of UdeM’s teaching hospital, the Centre hospitalier de l’Université de Montréal.

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

“Thanks to our data, we have successfully developed and validated a statistical model based on one blood biomarker,” viral RNA, Kaufmann adds. 

Despite breakthroughs in COVID-19 management, clinicians have struggled to identify patients most at risk of death. Several indicators have been identified in other studies, but juggling so many characteristics in a clinical context is impossible and slows clinicians down.

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

Chomont’s team measured the quantity of viral RNA, while Finzi’s team measured the levels of antiviral antibodies. Patients were monitored for a minimum of 60 days after samples were taken 11 days following the onset of symptoms.

“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,” says 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.”

When it came to testing the model, Kaufmann and Brunet-Ratnasingham ran it on two separate groups of patients from Montreal’s Jewish General Hospital and the CHUM (recruited during the second and third waves).

There was no variation in the predicted model’s performance depending on which hospital the patients were treated at or when the pandemic occurred. These findings are now being put to use by Kaufmann and his coworkers.

“It would be interesting to use the model to monitor patients,” he conludes, “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: 10.1126/sciadv.abj5629

Image Credit: iStock

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