Prediction of successful labor induction in persons with a low Bishop score using machine learning: Secondary analysis of two randomized controlled trials. Issue 1 (21st December 2022) - D'Souza, Rohan; Doyle, Orla; Miller, Hugh; Pillai, Natasha; Angehrn, Zuzanna; Li, Philip; Ispas‐Jouron, Simona
Abstract: Background: The objective of this paper was to identify predictors of a vaginal birth in individuals with singleton pregnancies and a Bishop Score <4, following Induction of Labor (IoL) using dinoprostone vaginal insert (DVI). Secondarily, we sought to understand the association between oxytocin use for labor augmentation and IoL outcomes. Methods: We developed and internally validated a multivariate prediction model using machine learning (ML) applied to data from two Phase‐III randomized controlled double‐blind trials (NCT01127581, NCT00308711). The model was internally validated using 10‐fold cross‐validation. Results: This study included 1107 participants. Despite unfavorable cervical status and inclusion of high‐risk pregnancies, 72% of participants had vaginal births. The model's area under receiver operating characteristic curve was 0.73. The following factors increased the chance of vaginal birth: being parous; being between 37 and 41 weeks of gestation; having a lower Body Mass Index; having a lower maternal age; having fewer maternal comorbidities; and having a higher Bishop score. Parity alone correctly predicted the outcome in ~50% of cases, at a ~10% false‐negative rate. Participants whose labors progressed without requiring oxytocin had a higher probability of vaginal birth than those requiring oxytocin for either induction or augmentation (81% vs 70% vs 77%, respectively). Discussion: Even in high‐risk pregnancies and with low Bishop scores, the use of DVI results in a high chance of vaginal birth. Parity is a critical predictor of success. The judicious use of oxytocin for labor induction or augmentation can increase the chance of vaginal birth. Our study validates the use of ML and predictive modeling for treatment response prediction when considering IoL .