Prediction of Extremely Preterm Birth Using Uterine Electromyography and Clinical Variables: Development of a Multifactorial Model, study suggests

Extremely preterm birth (EPB), defined as delivery before 28 weeks of gestation, poses significant challenges in maternal–fetal medicine, affecting approximately 2–5 per 1,000 pregnancies. This condition is associated with severe complications such as respiratory distress syndrome, severe neonatal sepsis, intraventricular hemorrhage, and long-term neurodevelopmental impairment. Therefore, the early identification of women at risk for EPB is crucial for improving perinatal management and resource allocation. Established predictors of EPB include a history of preterm delivery, assisted reproductive technology (ART) utilization, and shortened transvaginal cervical length (TVCL). However, these predictors primarily reflect past or static anatomical factors rather than real-time uterine contractions, indicating a need for non-invasive monitoring technologies.

Uterine electromyography (uEMG) has emerged as a promising non-invasive method to monitor uterine contractile activity through surface electrodes on the uterine myometrium. uEMG allows for the quantitative analysis of key contraction parameters and has the potential to detect uterine activity that traditional methods like manual palpation or tocodynamometry may overlook. This retrospective study aimed to evaluate the relationship between uEMG parameters—specifically contraction frequency, average peak contraction intensity, and average contraction duration—and the occurrence of EPB. Additionally, it sought to develop and validate a predictive model combining uEMG data with established clinical risk factors.

The study included 276 singleton pregnant women experiencing vaginal bleeding with or without lower abdominal pain, all of whom underwent uEMG monitoring between 20 and 27 weeks of gestation at Sun Yat-sen Memorial Hospital in Guangzhou, China, from January 2018 to May 2025. The findings indicated that higher contraction frequency and longer contraction duration, alongside history of ART and prior deliveries, significantly correlated with EPB (p < 0.05). The study resulted in two predictive models: one traditional model incorporating ART, prior deliveries, and TVCL, and an enhanced model integrating uEMG parameters.

Key findings revealed that out of 276 women, 36 developed EPB, and the multivariable analysis underscored the predictive power of contraction-related uEMG parameters. The model integrating uEMG demonstrated improved performance versus traditional models, with better discrimination as evidenced by an AUC-ROC of 0.859 compared to 0.716 for the traditional model (p < 0.05). Despite these promising results, the study faced limitations, including an observed higher EPB rate due to the cohort comprising high-risk individuals at a tertiary center and potential overfitting in model performance.

In conclusion, uEMG parameters serve as independent predictors of EPB, facilitating a novel, non-invasive approach to risk assessment. This review highlights the need for further validation of the uEMG model in larger, multicenter prospective studies before considering its routine clinical application.

BULLET POINTS:

*As a non-invasive and dynamic monitoring method, uEMG has the potential to complement established assessment tools in clinical practice.

*The predictive model, integrating these uEMG parameters with clinical indicators such as ART history, prior deliveries between 12 and 28 weeks, and TVCL, demonstrates better discrimination than a traditional clinical model alone.

*It may support more refined risk stratification and individualized management.

*uEMG parameters, specifically contraction frequency and average contraction duration, are independent predictors of EPB.

Reference : Tang J., Qi T., Li F.et al. Uterine electromyography as a new predictor of extremely preterm birth: a multifactorial model integrating clinical and bioelectrical parameters, BMC Pregnancy Childbirth (2025), https://doi.org/10.1186/s12884-025-08539-3