Development of a prediction nomogram for IgG levels among asymptomatic or mild patients with COVID-19.

Objective: COVID-19 has evolved into a seasonal coronavirus disease, characterized by prolonged infection duration and repeated infections, significantly increasing the risk of patients developing long COVID. Our research focused on the immune responses in asymptomatic and mild cases, particularly the critical factors influencing serum immunoglobulin G (IgG) levels and their predictive value. Methods: We conducted a retrospective analysis on data from 1939 asymptomatic or mildly symptomatic COVID-19 patients hospitalized between September 2022 and June 2023. Spearman methods were used to test the correlation between serum IgG and age, immunoglobulin M (IgM), procalcitonin (PCT), interleukin-6 (IL-6), nucleic acid conversion time, and BMI. Univariate and multivariate logistic regression analyses identified independent key factors influencing serum IgG levels, which were integrated and visualized in a nomogram. Finally, receiver operating characteristic (ROC) curves were plotted to predict the model's diagnostic performance by calculating the AUC. Results: Mild patients showed higher levels of IgG, IgM, and longer nucleic acid conversion times than asymptomatic patients, and a lower proportion of them had received ≥ 3 COVID-19 vaccine doses. Serum IgG was positively correlated with serum IgM and negatively correlated with age, PCT, IL-6, and BMI. Notably, it showed a moderate negative correlation with nucleic acid conversion time (r = -0.578, Conclusion: Age, IL-6 levels, vaccination status, and comorbidities were independent predictors of serum IgG levels in asymptomatic or mild COVID-19 patients, facilitating risk stratification and clinical decision-making. Notably, receiving ≥3 doses of the COVID-19 vaccine was the most beneficial factor for elevated serum IgG levels.