Impact of labor characteristics on maternal and neonatal outcomes of labor: A machine-learning model.

ABSTRACT:

Introduction

Since Friedman's seminal publication on laboring women, numerous publications have sought to define normal labor progress. However, there is paucity of data on contemporary labor cervicometry incorporating both maternal and neonatal outcomes. The objective of this study is to establish intrapartum prediction models of unfavorable labor outcomes using machine-learning algorithms.

Materials and methods

Consortium on Safe Labor is a large database consisting of pregnancy and labor characteristics from 12 medical centers in the United States. Outcomes, including maternal and neonatal outcomes, were retrospectively collected. We defined primary outcome as the composite of following unfavorable outcomes: cesarean delivery in active labor, postpartum hemorrhage, intra-

SUBMITTER: Shazly SA 

PROVIDER: S-EPMC9394788 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature