Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.

ABSTRACT:

Introduction

Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context.

Material and methods

We aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent valid

SUBMITTER: Ferreira I 

PROVIDER: S-EPMC11683543 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature