Prognostic value of severe acute respiratory syndrome coronavirus-2 viral load and antibodies in patients hospitalized with COVID-19

严重急性呼吸综合征冠状病毒2病毒载量和抗体对COVID-19住院患者的预后价值

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Abstract

Observational studies have identified the potential prognostic value for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral load and anti-SARS-CoV-2 antibodies in coronavirus disease 2019 (COVID-19). However, viral load in nasopharyngeal (NP) swabs produced inconsistent results in prognostic analyses, and the prognostic value of viral load or antibodies has not been confirmed in large clinical trials. COVACTA and REMDACTA were double-blind, randomized, controlled trials with a combined enrollment of 1078 patients hospitalized with COVID-19 treated with tocilizumab or placebo in COVACTA or tocilizumab plus remdesivir or placebo plus remdesivir in REMDACTA. We assessed the potential prognostic value of NP and serum SARS-CoV-2 viral load and serum anti-SARS-CoV-2 antibodies at baseline as biomarkers for clinical outcomes in patients enrolled in these trials. In adjusted Cox proportional hazard models, serum viral load was a more reliable predictor of clinical outcomes than NP viral load; high serum viral load was associated with higher risk for death and mechanical ventilation/death and lower likelihood of hospital discharge (high vs. negative viral load hazard ratios [95% confidence interval {CI}] were 2.87 [1.57-5.25], 3.86 [2.23-6.68], and 0.23 [0.14-0.36], respectively, in COVACTA and 8.11 [2.95-22.26], 10.29 [4.5-23.55], and 0.21 [0.15-0.29], respectively, in REMDACTA) and high serum viral load correlated with levels of inflammatory cytokines and lung damage biomarkers. High anti-SARS-CoV-2 spike protein antibody (ACOV2S) levels were associated with higher likelihood of hospital discharge (high vs. below the limit of quantification hazard ratios [95% CI] were 2.55 [1.59-4.08] for COVACTA and 1.54 [1.13-2.09] for REMDACTA). These results support the role of baseline SARS-CoV-2 serum viral load and ACOV2S antibody titers in predicting clinical outcomes for patients hospitalized with COVID-19.

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