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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

Abigail Flower
1   School of Data Science, University of Virginia, Charlottesville, Virginia
,
Daniel Vasiliu
2   Department of Mathematics, College of William & Mary, Williamsburg, Virginia
,
Tianrui Zhu
2   Department of Mathematics, College of William & Mary, Williamsburg, Virginia
,
Robert Andris
3   Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Maryam Abubakar
3   Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Karen Fairchild
3   Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Santina Zanelli
3   Department of Pediatrics, University of Virginia, Charlottesville, Virginia
,
Julie Matsumoto
4   Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia
,
Amit M. Mathur
5   Division of Neonatal-Perinatal Medicine, Saint Louis University, St. Louis, Missouri
,
John Delos
6   Department of Physics, College of William & Mary, Williamsburg, Virginia
,
Zachary Vesoulis 
7   Department of Pediatrics, Washington University St. Louis, St. Louis, Missouri
› Author Affiliations Funding This study received financial support from US Department of Health and Human Services, National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant number: R01 HD072071) and US Department of Health and Human Services, National Institutes of Health, National Institute of Neurological Disorders and Stroke (grant number: K23 NS111086).
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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

Supplementary Material



Publication History

Received: 10 January 2020

Accepted: 17 July 2020

Article published online:
20 August 2020

© 2020. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Kurinczuk JJ, White-Koning M, Badawi N. Epidemiology of neonatal encephalopathy and hypoxic-ischaemic encephalopathy. Early Hum Dev 2010; 86 (06) 329-338
    CrossrefPubMedGoogle Scholar
  • 2 Trivedi SB, Vesoulis ZA, Rao R. et al. A validated clinical MRI injury scoring system in neonatal hypoxic-ischemic encephalopathy. Pediatr Radiol 2017; 47 (11) 1491-1499
    CrossrefPubMedGoogle Scholar
  • 3 Weeke LC, Groenendaal F, Mudigonda K. et al. A novel magnetic resonance imaging score predicts neurodevelopmental outcome after perinatal asphyxia and therapeutic hypothermia. J Pediatr 2018; 192: 33-40.e2
    CrossrefPubMedGoogle Scholar
  • 4 Barkovich AJ, Hajnal BL, Vigneron D. et al. Prediction of neuromotor outcome in perinatal asphyxia: evaluation of MR scoring systems. AJNR Am J Neuroradiol 1998; 19 (01) 143-149
    PubMedGoogle Scholar
  • 5 Massaro AN, Govindan RB, Al-Shargabi T. et al. Heart rate variability in encephalopathic newborns during and after therapeutic hypothermia. J Perinatol 2014; 34 (11) 836-841
    CrossrefPubMedGoogle Scholar
  • 6 Goulding RM, Stevenson NJ, Murray DM, Livingstone V, Filan PM, Boylan GB. Heart rate variability in hypoxic ischemic encephalopathy: correlation with EEG grade and 2-y neurodevelopmental outcome. Pediatr Res 2015; 77 (05) 681-687
    CrossrefPubMedGoogle Scholar
  • 7 Aliefendioğlu D, Doğru T, Albayrak M, Dibekmısırlıoğlu E, Sanlı C. Heart rate variability in neonates with hypoxic ischemic encephalopathy. Indian J Pediatr 2012; 79 (11) 1468-1472
    CrossrefPubMedGoogle Scholar
  • 8 Vergales BD, Zanelli SA, Matsumoto JA. et al. Depressed heart rate variability is associated with abnormal EEG, MRI, and death in neonates with hypoxic ischemic encephalopathy. Am J Perinatol 2014; 31 (10) 855-862
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Berntson GG, Bigger Jr JT, Eckberg DL. et al. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 1997; 34 (06) 623-648
    CrossrefPubMedGoogle Scholar
  • 10 Parati G, Ochoa JE, Salvi P, Lombardi C, Bilo G. Prognostic value of blood pressure variability and average blood pressure levels in patients with hypertension and diabetes. Diabetes Care 2013; 36 (Suppl. 02) S312-S324
    CrossrefPubMedGoogle Scholar
  • 11 Mena LJ, Felix VG, Melgarejo JD, Maestre GE. 24-hour blood pressure variability assessed by average real variability: a systematic review and meta-analysis. J Am Heart Assoc 2017; 6 (10) e006895
    CrossrefPubMedGoogle Scholar
  • 12 Tan Z, Meng H, Dong D, Zhao Y, Xu A. Blood pressure variability estimated by ARV is a predictor of poor short-term outcomes in a prospective cohort of minor ischemic stroke. PLoS One 2018; 13 (08) e0202317
    CrossrefPubMedGoogle Scholar
  • 13 Murray DM, Boylan GB, Ryan CA, Connolly S. Early EEG findings in hypoxic-ischemic encephalopathy predict outcomes at 2 years. Pediatrics 2009; 124 (03) e459-e467
    CrossrefPubMedGoogle Scholar
  • 14 Sarnat HB, Sarnat MS. Neonatal encephalopathy following fetal distress. A clinical and electroencephalographic study. Arch Neurol 1976; 33 (10) 696-705
    CrossrefPubMedGoogle Scholar
  • 15 Murray DM, O'Connor CM, Ryan CA, Korotchikova I, Boylan GB. Early EEG grade and outcome at 5 years after mild neonatal hypoxic ischemic encephalopathy. Pediatrics 2016; 138 (04) e20160659
    CrossrefPubMedGoogle Scholar
  • 16 Hayakawa K, Koshino S, Tanda K. et al. Diffusion pseudonormalization and clinical outcome in term neonates with hypoxic-ischemic encephalopathy. Pediatr Radiol 2018; 48 (06) 865-874
    CrossrefPubMedGoogle Scholar
  • 17 Report of the second task force on blood pressure control in children--1987. task force on blood pressure control in children. National Heart, Lung, and Blood Institute, Bethesda, Maryland. Pediatrics 1987; 79 (01) 1-25
    CrossrefPubMedGoogle Scholar
  • 18 Cooper RJ, Selb J, Gagnon L. et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front Neurosci 2012; 6: 147
    CrossrefPubMedGoogle Scholar
  • 19 Ayaz H, Izzetoglu M, Shewokis PA, Onaral B. Sliding-window motion artifact rejection for functional near-infrared spectroscopy. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 6567-6570
    PubMedGoogle Scholar
  • 20 Bizzego A, Battisti A, Gabrieli G, Esposito G, Furlanello C. pyphysio: a physiological signal processing library for data science approaches in physiology. SoftwareX 2019; 10: 100287
    CrossrefPubMedGoogle Scholar
  • 21 Martin RJ, Fanaroff AA, Walsh MC. Fanaroff and Martin's Neonatal-Perinatal Medicine: Diseases of the Fetus and Infant. Philadelphia, PA: Saunders/Elsevier; 2011
    Google Scholar
  • 22 Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9 (01) 62-66
    CrossrefPubMedGoogle Scholar
  • 23 Vesoulis ZA, Hao J, McPherson C, El Ters NM, Mathur AM. Low-frequency blood pressure oscillations and inotrope treatment failure in premature infants. J Appl Physiol (1985) 2017; 123 (01) 55-61
    CrossrefPubMedGoogle Scholar
  • 24 Kvandal P, Landsverk SA, Bernjak A, Stefanovska A, Kvernmo HD, Kirkebøen KA. Low-frequency oscillations of the laser Doppler perfusion signal in human skin. Microvasc Res 2006; 72 (03) 120-127
    CrossrefPubMedGoogle Scholar
  • 25 Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci 1976; 39 (02) 447-462
    CrossrefPubMedGoogle Scholar
  • 26 Scargle JD. Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. APJ 1982; 263: 835
    CrossrefPubMedGoogle Scholar
  • 27 Malin GL, Morris RK, Khan KS. Strength of association between umbilical cord pH and perinatal and long term outcomes: systematic review and meta-analysis. BMJ 2010; 340: c1471
    CrossrefPubMedGoogle Scholar
  • 28 Laptook AR, Shankaran S, Ambalavanan N. et al; Hypothermia Subcommittee of the NICHD Neonatal Research Network. Outcome of term infants using apgar scores at 10 minutes following hypoxic-ischemic encephalopathy. Pediatrics 2009; 124 (06) 1619-1626
    CrossrefPubMedGoogle Scholar
  • 29 Natarajan G, Shankaran S, Laptook AR. et al; Extended Hypothermia Subcommittee of the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Apgar scores at 10 min and outcomes at 6-7 years following hypoxic-ischaemic encephalopathy. Arch Dis Child Fetal Neonatal Ed 2013; 98 (06) F473-F479
    CrossrefPubMedGoogle Scholar
  • 30 Zubrow AB, Hulman S, Kushner H, Falkner B. Philadelphia Neonatal Blood Pressure Study Group. Determinants of blood pressure in infants admitted to neonatal intensive care units: a prospective multicenter study. J Perinatol 1995; 15 (06) 470-479
    PubMedGoogle Scholar
  • 31 McKinstry RC, Miller JH, Snyder AZ. et al. A prospective, longitudinal diffusion tensor imaging study of brain injury in newborns. Neurology 2002; 59 (06) 824-833
    CrossrefPubMedGoogle Scholar
  • 32 Winter JD, Lee DS, Hung RM. et al. Apparent diffusion coefficient pseudonormalization time in neonatal hypoxic-ischemic encephalopathy. Pediatr Neurol 2007; 37 (04) 255-262
    CrossrefPubMedGoogle Scholar
  • 33 Devinsky O. Effects of seizures on autonomic and cardiovascular function. Epilepsy Curr 2004; 4 (02) 43-46
    CrossrefPubMedGoogle Scholar
  • 34 Tolaymat Y, Doré S, Griffin HW, Shih S, Edwards ME, Weiss MD. Inhaled gases for neuroprotection of neonates: a review. Front Pediatr 2020; 7: 558
    CrossrefPubMedGoogle Scholar
  • 35 Zhu C, Kang W, Xu F. et al. Erythropoietin improved neurologic outcomes in newborns with hypoxic-ischemic encephalopathy. Pediatrics 2009; 124 (02) e218-e226
    CrossrefPubMedGoogle Scholar
  • 36 Sewell EK, Vezina G, Chang T. et al. Evolution of amplitude-integrated electroencephalogram as a predictor of outcome in term encephalopathic neonates receiving therapeutic hypothermia. Am J Perinatol 2018; 35 (03) 277-285
    Article in Thieme ConnectPubMedGoogle Scholar
  • 37 Hilz MJ, DeFina PA, Anders S. et al. Frequency analysis unveils cardiac autonomic dysfunction after mild traumatic brain injury. J Neurotrauma 2011; 28 (09) 1727-1738
    CrossrefPubMedGoogle Scholar
  • 38 Kahraman S, Dutton RP, Hu P. et al. Heart rate and pulse pressure variability are associated with intractable intracranial hypertension after severe traumatic brain injury. J Neurosurg Anesthesiol 2010; 22 (04) 296-302
    CrossrefPubMedGoogle Scholar
  • 39 Papaioannou V, Giannakou M, Maglaveras N, Sofianos E, Giala M. Investigation of heart rate and blood pressure variability, baroreflex sensitivity, and approximate entropy in acute brain injury patients. J Crit Care 2008; 23 (03) 380-386
    CrossrefPubMedGoogle Scholar
  • 40 Vesoulis ZA, Ters NE, Foster A, Trivedi SB, Liao SM, Mathur AM. Response to dopamine in prematurity: a biomarker for brain injury?. J Perinatol 2016; 36 (06) 453-458
    CrossrefPubMedGoogle Scholar
  • 41 Semenova O, Lightbody G, O'Toole JM, Boylan G, Dempsey E, Temko A. Coupling between mean blood pressure and EEG in preterm neonates is associated with reduced illness severity scores. PLoS One 2018; 13 (06) e0199587
    CrossrefPubMedGoogle Scholar