A multiple regression model for predicting a high cytomegalovirus immunoglobulin G avidity level in pregnant women with IgM positivity

Abstract

Objective: To establish a model to predict high cytomegalovirus (CMV) immunoglobulin (Ig)G avidity index (AI) using clinical information, to contribute to the mental health of CMV-IgM positive pregnant women.

Methods: We studied 371 women with IgM positivity at ≤14 w of gestation. Information on the age, parity, occupation, clinical signs, IgM and G values, and IgG AI was collected. The IgG AI cut-off value for diagnosing congenital infection was calculated based on a receiver operating characteristic curve analysis. Between-group differences were assessed using the Mann-Whitney U-test or χ² analysis. The factors predicting a high IgG AI were determined using multiple logistic regression.

Results: The women were divided into high or low IgG AI groups based on an IgG AI cut-off value of 31.75. There were significant differences in the IgG and IgM levels, age, clinical signs, and the number of women with one parity between the two groups. In a multiple logistic regression analysis, IgM and the number of women with one parity were independent predictors. This result helped us establish a mathematical model that correctly classified the IgG AI level for 84.6% of women.

Conclusion: We established a highly effective model for predicting a high IgG AI immediately after demonstrating IgM positivity.

Keywords: Anxiety; Cytomegalovirus; Immunoglobulin M; Pregnancy; Screening.