A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction...

Area of Science:

• Reproductive Medicine
• Immunology
• Biomedical Data Science

Background:

• Preterm birth (PB) is a major cause of infant mortality and morbidity.
• Current prediction methods, primarily cervical length measurement, have limited detection rates (~70%).
• Cytokine-mediated inflammation is implicated in PB pathophysiology, but not clinically utilized for prediction.

Purpose of the Study:

• To investigate the role of specific cytokines in cervical-vaginal mucus as predictors of preterm birth.
• To develop and evaluate machine learning models for improved preterm birth prediction incorporating cytokine levels.

Main Methods:

• Quantified cytokines (IL-2, IL-6, IFN-γ, IL-4, IL-10, IL-1ra) in cervical-vaginal mucus from pregnant women (18-23.6 weeks gestation).
• Collected clinical obstetric data to stratify risk for PB based on cervical length.
• Developed Random Forest models for PB prediction using clinical variables and cytokine data.

Main Results:

• Elevated IL-2, IL-6, IFN-γ, IL-4, and IL-10, with lower IL-1ra, were observed in the high-risk PB group.
• An adjusted model using maternal age, IL-2, and cervical length achieved an 87% detection rate, outperforming the gold standard.
• The cytokine-inclusive model demonstrated improved performance metrics: higher detection rate (87% vs. 66%), lower false positive rate (3.33% vs. 12%), and lower false negative rate (6.66% vs. 28%).

Conclusions:

• Cytokine profiling in cervical-vaginal mucus can significantly enhance preterm birth prediction accuracy.
• Incorporating cytokines like IL-2 into predictive models, alongside clinical factors, offers a more effective approach to identifying at-risk pregnancies.
• This improved prediction accuracy may facilitate timely preventive interventions for preterm birth.