Papers

Peer-reviewed Lead author
Nov, 2020

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

International Journal of Infectious Diseases
  • Kaneko M
  • ,
  • Ohhashi M
  • ,
  • Fujii Y
  • ,
  • Minematsu T
  • ,
  • Kusumoto K

Volume
100
Number
First page
1
Last page
6
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.1016/j.ijid.2020.08.034
Publisher
International Journal of Infectious Diseases

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.

Link information
DOI
https://doi.org/10.1016/j.ijid.2020.08.034
URL
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090879863&origin=inward
ID information
  • DOI : 10.1016/j.ijid.2020.08.034
  • ISSN : 1201-9712

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