Impact of antibody-level on viral shedding in B.1.617.2 (Delta) variant-infected patients analyzed using a joint model of longitudinal and time-to-event data.

Abstract

Knowledge of viral shedding remains limited. Repeated measurement data have been rarely used to explore the influencing factors. In this study, a joint model was developed to explore and validate the factors influencing the duration of viral shedding based on longitudinal data and survival data. We divided 361 patients infected with Delta variant hospitalized in Nanjing Second Hospital into two groups (≤ 21 days group and > 21 days group) according to the duration of viral shedding, and compared their baseline characteristics. Correlation analysis was performed to identify the factors influencing the duration of viral shedding. Further, a joint model was established based on longitudinal data and survival data, and the Markov chain Monte Carlo algorithm was used to explain the influencing factors. In correlation analysis, patients having received vaccination had a higher antibody level at admission than unvaccinated patients, and with the increase of antibody level, the duration of viral shedding shortened. The linear mixed-effects model showed the longitudinal variation of logSARS-COV-2 IgM sample/cutoff (S/CO) values, with a parameter estimate of 0.193 and a standard error of 0.017. Considering gender as an influencing factor, the parameter estimate of the Cox model and their standard error were 0.205 and 0.1093 (P = 0.608), the corresponding OR value was 1.228. The joint model output showed that SARS-COV-2 IgM (S/CO) level was strongly associated with the risk of a composite event at the 95% confidence level, and a doubling of SARS-COV-2 IgM (S/CO) level was associated with a 1.38-fold (95% CI: [1.16, 1.72]) increase in the risk of viral non-shedding. A higher antibody level in vaccinated patients, as well as the presence of IgM antibodies in serum, can accelerate shedding of the mutant virus. This study provides some evidence support for vaccine prevention and control of COVID-19 variants.