Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy

Neonatal hypoxic-ischemic encephalopathy (HIE) is caused by sustained hypoxemia near birth. Clinical assessment using cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine pressure (UP), aims to identify infants at increased risk of HIE. Although CTG is nonstationary, current automated methods for its analysis use time invariant discrimination rules. Our objective was to examine the association between features of CTG and the development of HIE to determine if accounting for the time to delivery (TTD) would strengthen these associations. We analyzed 88 features extracted from FHR and UP signals from 25,197 vaginally delivered infants for whom blood gas measurements were available. All infants were categorized according to their blood gas exams into three mutually exclusive groups: 167 HIE, 1,912 acidosis - a precursor to HIE, and 22,903 healthy cases. We evaluated CTG features during the last twelve hours of labor to explore the associations between 1) CTG features and TTD, 2) CTG features and the development of HIE, and 3) the conditional association between CTG features and the development of HIE given TTD. These associations were quantified using the normalized mutual information. We found that all CTG features varied with TTD. Furthermore, 48 out of 88 features were not significantly associated with the outcome of labor and might not be useful in classification studies. We also found that 40 out of 88 features had significant associations with the development of HIE; accounting for TTD increased the association for 26 of these features. Therefore, automated methods for prediction of infants at risk of HIE should focus on this set of CTG features and account for their time-varying properties.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3570371
Primary Topic
Heart Rate Variability and Autonomic Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy

Marie‐Coralie Cornet, Robert E. Kearney, Heather Forquer, Johann Vargas-Calixto et al.
IEEE Access
Heart Rate Variability and Autonomic Control
article

Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy

Marie‐Coralie Cornet, Robert E. Kearney, Heather Forquer, Johann Vargas-Calixto, Lawrence Gerstley, Philip Warrick, Michael W. Kuzniewicz, Tai-Wei Wu
article en

Abstract

Neonatal hypoxic-ischemic encephalopathy (HIE) is caused by sustained hypoxemia near birth. Clinical assessment using cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine pressure (UP), aims to identify infants at increased risk of HIE. Although CTG is nonstationary, current automated methods for its analysis use time invariant discrimination rules. Our objective was to examine the association between features of CTG and the development of HIE to determine if accounting for the time to delivery (TTD) would strengthen these associations. We analyzed 88 features extracted from FHR and UP signals from 25,197 vaginally delivered infants for whom blood gas measurements were available. All infants were categorized according to their blood gas exams into three mutually exclusive groups: 167 HIE, 1,912 acidosis - a precursor to HIE, and 22,903 healthy cases. We evaluated CTG features during the last twelve hours of labor to explore the associations between 1) CTG features and TTD, 2) CTG features and the development of HIE, and 3) the conditional association between CTG features and the development of HIE given TTD. These associations were quantified using the normalized mutual information. We found that all CTG features varied with TTD. Furthermore, 48 out of 88 features were not significantly associated with the outcome of labor and might not be useful in classification studies. We also found that 40 out of 88 features had significant associations with the development of HIE; accounting for TTD increased the association for 26 of these features. Therefore, automated methods for prediction of infants at risk of HIE should focus on this set of CTG features and account for their time-varying properties.

IEEE AccessVol. 13
Kaiser Permanente (US), University of California, San Francisco (US), McGill University (CA)
Bill and Melinda Gates Foundation, Kaiser Permanente, McGill University, National Institutes of Health
Zero hunger
Openalex Percentile: Top 81%
Heart Rate Variability and Autonomic Control
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