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Background <p>Group B Streptococcus (GBS) is a key pathogen in perinatal health. This study aimed to develop and internally validate a risk prediction model for positive reproductive tract GBS screening in late-pregnancy women using routinely collected clinical data.</p> Methods <p>This single-center retrospective study included 363 late-pregnancy women who underwent combined vaginal-rectal GBS screening from January 2023 to May 2024. Data were split 7:3 into training (<i>n</i> = 254) and validation (<i>n</i> = 109) sets using stratified sampling. Predictive variables were selected using least absolute shrinkage and selection operator (LASSO) regression combined with the Boruta algorithm. Seven machine learning models were developed in the training set. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, Brier score, calibration curves, and decision curve analysis.</p> Results <p>The GBS positivity rate was 25.1% (91/363). Feature selection identified seven predictors: education level, platelet count, lymphocyte count, vaginitis type, abnormal vaginal discharge, gestational thyroid disease, and gestational weight gain. The random forest model showed the best performance in the validation set, achieving an AUC of 0.868 (95% CI: 0.811–0.922) and a Brier score of 0.153.</p> Conclusions <p>The random forest model, built from routinely available clinical data, demonstrated relatively favorable discrimination and calibration in internal validation for predicting positive GBS screening in late pregnancy. It may offer potential for risk stratification and screening adherence management, but it should not replace standard microbiological testing or clinical judgment. Multicenter prospective validation is required before clinical application.</p>

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Associated factors and machine learning-based prediction model construction for positive reproductive tract group B streptococcus screening in late-pregnancy women

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Fangfang Liu,
  • Lixia Wu,
  • Fang Li,
  • Qianqian He,
  • Xiaofen Duan,
  • Jing Gao

摘要

Background

Group B Streptococcus (GBS) is a key pathogen in perinatal health. This study aimed to develop and internally validate a risk prediction model for positive reproductive tract GBS screening in late-pregnancy women using routinely collected clinical data.

Methods

This single-center retrospective study included 363 late-pregnancy women who underwent combined vaginal-rectal GBS screening from January 2023 to May 2024. Data were split 7:3 into training (n = 254) and validation (n = 109) sets using stratified sampling. Predictive variables were selected using least absolute shrinkage and selection operator (LASSO) regression combined with the Boruta algorithm. Seven machine learning models were developed in the training set. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, Brier score, calibration curves, and decision curve analysis.

Results

The GBS positivity rate was 25.1% (91/363). Feature selection identified seven predictors: education level, platelet count, lymphocyte count, vaginitis type, abnormal vaginal discharge, gestational thyroid disease, and gestational weight gain. The random forest model showed the best performance in the validation set, achieving an AUC of 0.868 (95% CI: 0.811–0.922) and a Brier score of 0.153.

Conclusions

The random forest model, built from routinely available clinical data, demonstrated relatively favorable discrimination and calibration in internal validation for predicting positive GBS screening in late pregnancy. It may offer potential for risk stratification and screening adherence management, but it should not replace standard microbiological testing or clinical judgment. Multicenter prospective validation is required before clinical application.