Published in

Springer Nature [academic journals on nature.com], Pediatric Research, 2(70), p. 166-170, 2011

DOI: 10.1203/pdr.0b013e3182231d9e

Links

Tools

Export citation

Search in Google Scholar

Applicability of Near-Infrared Spectroscopy to Measure Cerebral Autoregulation Noninvasively in Neonates: A Validation Study in Piglets

Journal article published in 2011 by Gitte H. Hahn, Christian Heiring, Ole Pryds, Gorm Greisen ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Impaired cerebral autoregulation (CA) is common and is associated with brain damage in sick neonates. Frequency analysis using spontaneous changes in arterial blood pressure (ABP) and cerebral near-infrared spectroscopy (NIRS) has been used to measure CA in several clinical studies. Coherence of the NIRS and ABP signals (i.e. correlation in the frequency domain) detects impairment of CA, whereas gain (i.e. magnitude of ABP variability passing from systemic to cerebral circulation) estimates the degree of this impairment. So far, however, this method has not been validated. In 12 newborn piglets, we compared NIRS-derived measures of CA with a conventional measure of CA: cerebral blood flow was measured by laser Doppler flowmetry, and changes in ABP were induced by inflating a thoracic aorta balloon. CA capacity was calculated as %ΔCVR/%ΔABP (i.e. percentage of full autoregulatory capacity), where CVR (i.e. cerebral vascular resistance) was estimated as ABP/Doppler flux. Correlation between coherence and CA capacity (r = -0.34, n = 24, p > 0.05) and between gain and CA capacity (r = -0.11, n = 24, p > 0.05) was limited. As expected, however, gain was significantly associated with CA capacity in measurements with significant coherence (r = -0.55, n = 15, p = 0.03). In conclusion, our data validate frequency analysis for estimation of CA in clinical research. Low precision, however, hampers its clinical application.