An Exploratory Approach of Clinically Useful Biomarkers of Cvid by Logistic Regression

Abstract

Despite improvements in genetic and functional studies, delayed diagnosis of common variable immunodeficiency (CVID) remains challenging. To overcome this, an exploratory study to evaluate the diagnostic performance of a panel of biomarkers for CVID, such as the sum of κ+λlight chains and the soluble B-cell maturation antigen (sBCMA) levels, switched memory B cells (smB) and VISUAL score, through logistic regression models compared to gold-standard tests (specific antibody responses) was carried out.ANOVA and bivariate analysis were performed between different groups and logistic regression models were fitted using CVID biomarkers between CVID and selective IgA deficiency (SIgAD). A total of 88 subjects were studied: 27 CVID patients, 23 SIgAD patients, 20 secondary immunodeficiency (SID) patients and 18 healthy controls. We validated the diagnostic performance of individual biomarkers sBCMA and sum κ+λ, with Se 89% and Spe 89%, versus Se 90% and Spe 99%, respectively. sBCMA strongly correlated with all other three variables (sum κ+λ, smB cell and VISUAL). By contrast, sum κ+λ did not correlate with either smB cells or VISUAL, and could provide added diagnostic value. By multivariable tree decision model, only 2 two factors proved to be independent signature biomarkers of CVID, namely specific antibody responses and sum κ+λ. The resulting model had an AUC of 0.946, Se 0.85, and Spe 0.95. The tree-decision model can increase diagnostic efficiency. Sum κ+λ stood out over other CVID classifiers, further highlighting its potential as a diagnostic criterion.