# Predicting Intrapartum Acidemia: A Review of Approaches Based on Fetal Heart Rate

## TL;DR

This review explores methods to predict fetal acidemia during labor using fetal heart rate and other tools to improve clinical decision-making.

## Contribution

The paper systematically reviews 30 studies to evaluate the effectiveness of fetal heart rate features and machine learning in predicting acidemia.

## Key findings

- Clinical CTG guidelines often result in intermediate risk classifications, relying on clinician experience.
- Fetal heart rate features can effectively distinguish acidemic from healthy fetuses.
- Machine learning models show promise in integrating fHR features to improve acidemia prediction.

## Abstract

Fetal acidemia, caused by impaired gas exchange between the fetus and the mother, is a leading cause of stillbirth and neurologic complications. Early prediction is therefore essential to guide timely clinical intervention. Several strategies rely on cardiotocography (CTG), which combines fetal heart rate (fHR) with uterine contractions and has led to development of clinical guidelines for CTG interpretation and the introduction of different fHR features. Additionally, ST event analysis, investigating changes in the ST segments of the fetal electrocardiogram (fECG), has been proposed as a complementary tool. This narrative review adopts a systematic approach, with comprehensive searches in Embase and PubMed to ensure full coverage of the available literature, and summarizes findings from 30 studies. Clinical guidelines for CTG interpretation frequently lead to intermediate risk level annotations, leaving the final decision regarding fetal management to clinical experience. In contrast, various fHR features can successfully discriminate between fetuses developing acidemia and healthy controls. Evidence regarding the added value of ST events derived from the scalp electrode remains conflicting, due to concerns about invasiveness. Recent studies on machine learning models highlight their ability to integrate multiple fHR features and improve predictive performance, suggesting a promising direction for enhancing acidemia prediction during labor.