Low Variability of Blood Pressure Predicts Abnormal Electroencephalogram in Infants with Hypoxic Ischemic Encephalopathy

Abigail Flower; Daniel Vasiliu; Tianrui Zhu; Robert Andris; Maryam Abubakar; Karen Fairchild; Santina Zanelli; Julie Matsumoto; Amit M. Mathur; John Delos; Zachary Vesoulis

doi:10.1055/s-0040-1715822

American Journal of Perinatology, Table of Contents

Am J Perinatol 2022; 39(03): 288-297
DOI: 10.1055/s-0040-1715822

Original Article

Low Variability of Blood Pressure Predicts Abnormal Electroencephalogram in Infants with Hypoxic Ischemic Encephalopathy

Authors

Abigail Flower

¹School of Data Science, University of Virginia, Charlottesville, Virginia
Daniel Vasiliu

²Department of Mathematics, College of William & Mary, Williamsburg, Virginia
Tianrui Zhu

²Department of Mathematics, College of William & Mary, Williamsburg, Virginia
Robert Andris

³Department of Pediatrics, University of Virginia, Charlottesville, Virginia
Maryam Abubakar

³Department of Pediatrics, University of Virginia, Charlottesville, Virginia
Karen Fairchild

³Department of Pediatrics, University of Virginia, Charlottesville, Virginia
Santina Zanelli

³Department of Pediatrics, University of Virginia, Charlottesville, Virginia
Julie Matsumoto

⁴Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia
Amit M. Mathur

⁵Division of Neonatal-Perinatal Medicine, Saint Louis University, St. Louis, Missouri
John Delos

⁶Department of Physics, College of William & Mary, Williamsburg, Virginia
Zachary Vesoulis

⁷Department of Pediatrics, Washington University St. Louis, St. Louis, Missouri

Abstract

Objective This study aimed to evaluate the role of an objective physiologic biomarker, arterial blood pressure variability, for the early identification of adverse short-term electroencephalogram (EEG) outcomes in infants with hypoxic-ischemic encephalopathy (HIE).

Study Design In this multicenter observational study, we analyzed blood pressure of infants meeting these criteria: (1) neonatal encephalopathy determined by modified Sarnat exam, (2) continuous mean arterial blood pressure (MABP) data between 18 and 27 hours after birth, and (3) continuous EEG performed for at least 48 hours. Adverse outcome was defined as moderate–severe grade EEG at 48 hours. Standardized signal preprocessing was used; the power spectral density was computed without interpolation. Multivariate binary logistic regression was used to identify which MABP time and frequency domain metrics provided improved predictive power for adverse outcomes compared with standard clinical predictors (5-minute Apgar score and cord pH) using receiver operator characteristic analysis.

Results Ninety-one infants met inclusion criteria. The mean gestational age was 38.4 ± 1.8 weeks, the mean birth weight was 3,260 ± 591 g, 52/91 (57%) of infants were males, the mean cord pH was 6.95 ± 0.21, and 10/91 (11%) of infants died. At 48 hours, 58% of infants had normal or mildly abnormal EEG background and 42% had moderate or severe EEG backgrounds. Clinical predictor variables (10-minute Apgar score, Sarnat stage, and cord pH) were modestly predictive of 48 hours EEG outcome with area under curve (AUC) of 0.66 to 0.68. A composite model of clinical and optimal time- and frequency-domain blood pressure variability had a substantially improved AUC of 0.86.

Conclusion Time- and frequency-domain blood pressure variability biomarkers offer a substantial improvement in prediction of later adverse EEG outcomes over perinatal clinical variables in a two-center cohort of infants with HIE.

Key Points

Early outcome prediction in HIE is suboptimal.
Patterns in blood pressure physiology may be predictive of short-term outcomes.
Early time- and frequency-domain measures of blood pressure variability predict short-term EEG outcomes in HIE infants better than perinatal factors alone.

Keywords

neonates - neurology - EEG - blood pressure - hypoxic ischemic encephalopathy

Full Text

References

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