Using transperineal ultrasound to predict labor onset

A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study

OBJECTIVE

This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms.

METHODS

In this retrospective observational study, data on nulliparous women without contraindications for vaginal delivery with a singleton pregnancy ≥37 weeks and before the onset of labor from September 2020 to September 2021 were divided into a training set and a temporal validation set. Transperineal ultrasound was performed to collect angle of progression, head-perineum distance, subpubic arch angle, and their levator hiatal dimensions. The cervical length was measured via transvaginal ultrasound. The delivery methods were later recorded. Through LASSO regression analysis, indicators that can affect SVD failure were selected. Seven common machine learning algorithms were selected for model training, and the optimal algorithm was selected based on the area under the curve (AUC) to evaluate the effectiveness of the validation model.

RESULTS

Four indicators related to SVD failure were identified through LASSO regression screening: angle of progression, cervical length, subpubic arch angle, and estimated fetal weight. The Gaussian NB algorithm was found to yield the highest AUC (0.82, 95% confidence interval [CI] 0.65-0.98) during model training, and hence it was chosen for verification with the temporal validation set, in which an AUC of 0.79 (95% CI 0.64-0.95) was obtained with accuracy, sensitivity, and specificity rates of 80.9%, 72.7%, and 75.0%, respectively.

CONCLUSION

The Gaussian NB model showed good predictive effect, proving its potential as a clinical reference for predicting SVD failure of term nulliparous women before actual delivery.

Predicting labor onset relative to the estimated date of delivery using smart ring physiological data

The transition from pregnancy into parturition is physiologically directed by maternal, fetal and placental tissues. We hypothesize that these processes may be reflected in maternal physiological metrics. We enrolled pregnant participants in the third-trimester (n = 118) to study continuously worn smart ring devices monitoring heart rate, heart rate variability, skin temperature, sleep and physical activity from negative temperature coefficient, 3-D accelerometer and infrared photoplethysmography sensors. Weekly surveys assessed labor symptoms, pain, fatigue and mood. We estimated the association between each metric, gestational age, and the likelihood of a participant's labor beginning prior to (versus after) the clinical estimated delivery date (EDD) of 40.0 weeks with mixed effects regression. A boosted random forest was trained on the physiological metrics to predict pregnancies that naturally passed the EDD versus undergoing onset of labor prior to the EDD. Here we report that many raw sleep, activity, pain, fatigue and labor symptom metrics are correlated with gestational age. As gestational age advances, pregnant individuals have lower resting heart rate 0.357 beats/minute/week, 0.84 higher heart rate variability (milliseconds) and shorter durations of physical activity and sleep. Further, random forest predictions determine pregnancies that would pass the EDD with accuracy of 0.71 (area under the receiver operating curve). Self-reported symptoms of labor correlate with increased gestational age and not with the timing of labor (relative to EDD) or onset of spontaneous labor. The use of maternal smart ring-derived physiological data in the third-trimester may improve prediction of the natural duration of pregnancy relative to the EDD.

Prediction of Successful Induction of Labor using Ultrasonic Fetal Parameters

Background:

Induction of labor (IOL) is a common obstetrical procedure. Bishop's score was the single predictor element used by practitioners to assess the risk of failure, which led to an increase in cesarean sections (CS). Ultrasound (US) examination was proposed since the variability limitations of Bishop score warranted alternative assessment tools.

Objective:

This study verifies how the US and other maternal parameters are used in the transperineal approach as an indication and as a predictor of successful induction.

Material and methods:

A prospective clinical study of 100 participants with term singleton pregnancy referred for IOL and who fit the criteria of this study. Their maternal parameters and fetal head to perineum distance (HPD), measured by the transperineal US, were calculated before the induction. After the induction, the patients were stratified into two groups, which are vaginal delivery (68%) and CS (32%). The estimated time interval to delivery was also recorded.

Results:

None of the maternal parameters was significant; the P-values of maternal age, parity, body mass index (BMI), gestational age, and weight of the fetus is 0.75, 0.75, 0.69, 0.81, and 0.81, respectively. One-way ANOVA test estimated the most significant factors for inducing labor. Fetal HPD and induction to delivery interval were remarkably significant in both groups <0.0001.

Conclusion:

The shorter the HPD (<47.65±1.66 mm), the higher the possibility of vaginal delivery and a shorter delivery interval. By contrast, the longer HPD (>52.56±1.93mm), the lower the possibility of vaginal delivery and a longer delivery interval. These promising results may serve as a valuable tool in predicting a mode of delivery.