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Updated: May 17, 2026

Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice
04:18

Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice

Published on: October 10, 2025

Regression-based model for predicting preterm birth using vaginal lactobacilli and routine clinical data.

Lihua Kang1,2, Yuan Su1, Shu Li1

  • 1Department of Clinical Laboratory, Chongqing Health Center for Women and Children's Hospital of Chongqing Medical University, Chongqing, 400014, P.R. China.

BMC Pregnancy and Childbirth
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

A new model combining vaginal microbiota and clinical data shows promise for predicting preterm birth risk. This approach integrates microbial and routine prenatal indicators for improved prenatal risk stratification.

Keywords:
Cost-effectivePrediction modelPreterm birthRoutine Clinical DataVaginal microbiota

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Area of Science:

  • Obstetrics and Gynecology
  • Microbiome Research
  • Clinical Prediction Modeling

Background:

  • Preterm birth (PTB) is a major cause of neonatal morbidity and mortality.
  • Existing research links vaginal microbiota to PTB, but practical predictive models are lacking.
  • This study aimed to develop a model integrating microbial and clinical data for PTB risk assessment.

Purpose of the Study:

  • To develop and validate a predictive model for preterm birth risk.
  • To integrate vaginal microbiota profiles with routine clinical indicators.
  • To enhance prenatal risk stratification for preterm birth.

Main Methods:

  • Retrospective cohort study of 4,558 pregnant women (1,228 in final analysis).
  • Included clinical variables (blood counts, biochemical tests) and vaginal microbiota data.
  • Used logistic, LASSO, and Poisson regression models with non-parametric imputation for missing data.

Main Results:

  • Reduced vaginal Lactobacillus, vaginal fluid leakage, abnormal blood glucose, and elevated Cystatin C were associated with increased PTB risk.
  • A logistic model integrating vaginal microecological imbalance indicators and clinical variables achieved an AUC of 0.753.
  • This suggests improved accuracy in prenatal PTB risk assessment by combining microbial and clinical data.

Conclusions:

  • A prediction model integrating routine prenatal indicators and vaginal microbiota was developed.
  • The model offers a preliminary, feasible, and low-cost framework for prenatal risk stratification.
  • Further multicenter validation is required before clinical application.