Integrated immunovirological profiling validates plasma SARS-CoV-2 RNA as an early predictor of COVID-19 mortality

Brunet-Ratnasingham, E.; Anand, S. P.; Gantner, P.; Moquin-Beaudry, G.; Dyachenko, A.; Brassard, N.; Beaudoin-Bussieres, G.; Pagliuzza, A.; Gasser, R.; Benlarbi, M.; Point, F.; Prevost, J.; Laumaea, A.; Niessl, J.; Nayrac, M.; Sannier, G.; Boutin, M.; Descoteaux-Dinelle, J.; Gendron, G.; Orban, C.; Butler-Laporte, G.; Morrison, D.; Zhou, S.; Nakanishi, T.; Laurent, L.; Richard, J.; Dube, M.; Fromentin, R.; Rebillard, R.-M.; Arbour, N.; Prat, A.; Larochelle, C.; Durand, M.; Richards, B.; Chasse, M.; Tetreault, M.; Chomont, N.; Finzi, A.; Kaufmann, D. E.

Despite advances in COVID-19 management, it is unclear how to recognize patients who evolve towards death. This would allow for better risk stratification and targeting for early interventions. However, the explosive increase in correlates of COVID-19 severity complicates biomarker prioritisation. To identify early biological predictors of mortality, we performed an immunovirological assessment (SARS-CoV-2 viral RNA, cytokines and tissue injury markers, antibody responses) on plasma samples collected from 144 hospitalised COVID-19 patients 11 days after symptom onset and used to test models predicting mortality within 60 days of symptom onset. In the discovery cohort (n=61, 13 fatalities), high SARS-CoV-2 vRNA, low RBD-specific IgG levels, low SARS-CoV-2-specific antibody-dependent cellular cytotoxicity, and elevated levels of several cytokines and lung injury markers were strongly associated with increased mortality in the entire cohort and the subgroup on mechanical ventilation. Model selection revealed that a three-variable model of vRNA, age and sex was very robust at identifying patients who will succumb to COVID-19 (AUC=0.86, adjusted HR for log-transformed vRNA=3.5; 95% CI: 2.0-6.0). This model remained robust in an independent validation cohort (n=83, AUC=0.85). Quantification of plasma SARS-CoV-2 RNA can help understand the heterogeneity of disease trajectories and identify patients who may benefit from new therapies.