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Author Notes:

Lisa Stroux, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK, Mobile: 0044 7851696157, lisa.stroux@web.de.

The authors report no conflict of interest.

Subjects:

Research Funding:

LS acknowledges the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation).

AG was supported by the Action Medical Research and the Henry Smith Charity.

GC acknowledges the support of the National Institutes of Health, the Fogarty International Center and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant number 1R21HD084114-01.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Obstetrics & Gynecology
  • Cardiotocography
  • Doppler
  • heart rate variability
  • intrauterine growth restriction
  • low-cost automated fetal monitoring
  • SYMPHYSIS-FUNDAL HEIGHT
  • BEHAVIORAL STATES
  • BIRTH-WEIGHT
  • FETUSES
  • OUTCOMES
  • TRUFFLE
  • AVERAGE
  • SLEEP
  • TRIAL
  • FLOW

Doppler-based fetal heart rate analysis markers for the detection of early intrauterine growth restriction

Tools:

Journal Title:

Acta Obstetricia et Gynecologica Scandinavica

Volume:

Volume 96, Number 11

Publisher:

, Pages 1322-1329

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Introduction: One indicator for fetal risk of mortality is intrauterine growth restriction (IUGR). Whether markers reflecting the impact of growth restriction on the cardiovascular system, computed from a Doppler-derived heart rate signal, would be suitable for its detection antenatally was studied. Material and methods: We used a cardiotocography archive of 1163 IUGR cases and 1163 healthy controls, matched for gestation and gender. We assessed the discriminative power of short-term variability and long-term variability of the fetal heart rate, computed over episodes of high and low variation aiming to separate growth-restricted fetuses from controls. Metrics characterizing the sleep state distribution within a trace were also considered for inclusion into an IUGR detection model. Results: Significant differences in the risk markers comparing growth-restricted with healthy fetuses were found. When used in a logistic regression classifier, their performance for identifying IUGR was considerably superior before 34 weeks of gestation. Long-term variability in active sleep was superior to short-term variability [area under the receiver operator curve (AUC) of 72% compared with 71%]. Most predictive was the number of minutes in high variation per hour (AUC of 75%). A multivariate IUGR prediction model improved the AUC to 76%. Conclusion: We suggest that heart rate variability markers together with surrogate information on sleep states can contribute to the detection of early-onset IUGR.

Copyright information:

© 2017 Nordic Federation of Societies of Obstetrics and Gynecology

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