Improved Predictive Capacity of SARS-CoV-2 Seroconversion Using a Model Based on Torque Teno Virus Viral Load.

چکیده اصلی

INTRODUCTION: Kidney transplant patients (KTPs) exhibit markedly reduced seroconversion rates following SARS-CoV-2 vaccination due to chronic immunosuppression. Torque Teno virus (TTV) viral load has been proposed as a biomarker of immune competence. OBJECTIVES: To determine the seroconversion rate to SARS-CoV-2 vaccination in KTPs and to develop a predictive model based on TTV viral load. METHODS: A prospective observational cohort study was conducted in adult KTPs with complete SARS-CoV-2 vaccination scheme. Anti-Spike IgG antibodies were quantified. TTV viral load was quantified by qPCR. Logistic regression models were developed using TTV viral load alone and with patients' variables (expanded model). Model performance was assessed using receiver operating characteristic analysis and internal validation. RESULTS: Seroconversion rate was 16.9% after complete vaccination, 21.9% after one booster and 55.5% after two boosters. TTV viral load alone showed modest predictive capacity for seroconversion (AUC = 0.634). The expanded model significantly improved prediction capacity (AUC = 0.814) with a NPV of 90.0%. CONCLUSION: Pre-vaccination TTV viral load alone is insufficient to predict seroconversion to SARS-CoV-2 vaccination in KTPs. The expanded model improved this predictive capacity when combined with clinical and vaccine related variables reflecting the multifactorial nature of immune competence. Integrative predictive approaches may help optimize vaccination strategies in immunocompromised populations.

متن کامل اصلی