Ultrasound assessment of cervical status compared to the Bishop score - predicting the success of labor induction using a machine learning-based model.

OBJECTIVE: The main aim of this study was to develop a machine-learning-based model for predicting the success of labor induction (IOL). To that end, the clinical and ultrasound parameters that affect the successfulness of labor induction were assessed. Then, a new ultrasound scoring system (USS) was developed and assessed. PATIENTS AND METHODS: This prospective observational study included 192 term women who underwent induction of labor. First, a wide range of clinical and ultrasound pre-induction parameters were recorded. The induction was initiated by endocervical administration of dinoprostone gel (for Bishop score ≤5) or intravenous oxytocin (for Bishop score ≥6). After evaluating ultrasound parameters, we created an ultrasound scoring system and compared it with the Bishop score and clinical parameters. Finally, a comprehensive model using machine learning algorithms for predicting the success of the induction of labor was developed. RESULTS: In terms of clinical parameters, this study found that IOL correlates with parity, body mass index (BMI) (both at p<0.05), and the Bishop score (p<0.001). All ultrasound parameters were statistically significant (p<0.05) apart from the posterior cervical angle. However, compared to the Bishop score, the new USS showed a slightly lower sensitivity (0.55 compared to 0.64) but much higher specificity (0.75 compared to 0.44) at a cut-off of 1.66. The proposed model, which can predict 83% of the events correctly, encompasses the Bishop score, USS, and clinical parameters. CONCLUSIONS: The findings imply that the model developed in this study, which takes into account clinical parameters (parity, BMI), the ultrasound parameters and the Bishop score and uses machine learning algorithms, yields better results than models using other parameters.

Correlation between increased maternal body mass index and pregnancy complications.

OBJECTIVE: Obesity in pregnancy carries significant maternal and fetal risk. The aim of this study was to investigate the effect of maternal body mass index on pregnancy outcomes. PATIENTS AND METHODS: The study retrospectively reviewed the clinical outcome of 485 pregnant women who delivered at the Department of Obstetrics and Gynecology, Clinical Centre of Vojvodina, Novi Sad, during the period of three years (2018-2020) and compared them against the body mass index (BMI). Correlation coefficient was calculated for BMI and seven pregnancy complications (hypertensive syndrome, preeclampsia, gestational diabetes mellitus, intrauterine growth restriction, premature rupture of membranes, mode of delivery and postpartum hemorrhage). The collected data were presented in the form of median values and relative numbers (the measure of variability). The implementation of the simulation model and its verification were carried out using a specialized programming language, Python. Statistical models were created where the Chi-square and p-value were as determined for every observed outcome. RESULTS: The average age of the subjects was 35.79 years and average BMI 29.28 kg/m2. A statistically significant correlation was found between the BMI and arterial hypertension, gestational diabetes mellitus, preeclampsia and cesarean section. The correlations between the body mass index and postpartum hemorrhage, intrauterine growth restriction and premature rupture of membranes were not statistically significant. CONCLUSIONS: As high BMI correlates with a number of negative outcomes in pregnancy, weight control before and during pregnancy and proper antenatal and intranatal care are necessary to achieve a favorable pregnancy outcome.