Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden.

Journal: Acta obstetricia et gynecologica Scandinavica
PMID: 33031579

Abstract

INTRODUCTION: Predicting a woman's probability of vaginal birth after cesarean could facilitate the antenatal decision-making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only a cesarean delivery is more unpredictable. Therefore, to better predict vaginal birth in women with only one prior cesarean delivery and no vaginal deliveries would greatly benefit clinical practice and fill a key evidence gap in research. Our aim was to predict vaginal birth in women with one prior cesarean and no vaginal deliveries using machine-learning methods, and compare with a US prediction model and its further developed model for a Swedish setting.

Authors

  • Charlotte Lindblad Wollmann
    Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Kyle D Hart
    Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA.
  • Can Liu
    School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei, Anhui 230012, China.
  • Aaron B Caughey
    Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA.
  • Olof Stephansson
    Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Jonathan M Snowden
    Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA.

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