A multiple regression model for predicting a high cytomegalovirus immunoglobulin G avidity level in pregnant women with IgM positivity
| Autor: | Kazumi Kusumoto, Toshio Minematsu, Masanao Ohhashi, Masatoki Kaneko, Yoshinori Fujii |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
0301 basic medicine Microbiology (medical) medicine.medical_specialty 030106 microbiology Antibody Affinity Cytomegalovirus Anxiety Antibodies Viral Logistic regression lcsh:Infectious and parasitic diseases 03 medical and health sciences 0302 clinical medicine Pregnancy medicine Humans lcsh:RC109-216 Avidity 030212 general & internal medicine Pregnancy Complications Infectious biology Receiver operating characteristic business.industry Obstetrics General Medicine medicine.disease Logistic Models Infectious Diseases Immunoglobulin M ROC Curve Immunoglobulin G Cytomegalovirus Infections Screening biology.protein Gestation Female Antibody Parity (mathematics) business |
| Zdroj: | International Journal of Infectious Diseases, Vol 100, Iss, Pp 1-6 (2020) |
| ISSN: | 1201-9712 |
| DOI: | 10.1016/j.ijid.2020.08.034 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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