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Session: Brain
Session starts: Friday 27 January, 14:00
Presentation starts: 14:30
Room: Room 558

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Nick Eleveld (UMCG)
Gea Drost (UMCG)
Anthony Absalom (UMCG)
Clark Zeebregts (UMCG)
Jean-Paul de Vries (UMCG)
Natasha Maurits (UMCG)
Jan Willem Elting (UMCG)

Abstract:
Background: Dynamic cerebral autoregulation (DCA) is the brain’s ability to maintain adequate cerebral perfusion in the face of blood pressure changes over time. DCA is traditionally quantified with transcranial doppler (TCD) and arterial blood pressure (ABP) measurements. We have recently developed a simpler DCA-method based on near-infrared spectroscopy (NIRS-DCA) measurements1 and are currently validating this method in a clinical population undergoing carotid end-arterectomy (CEA, plaque removal). CEA is an interesting clinical model for validation because cross-clamping of the carotid artery can be required, which leads to a profound change in cerebral perfusion. Methods: This is an ongoing two-centre prospective observational study in 50 patients undergoing CEA. After the induction of anaesthesia, we perform continuous-wave NIRS-measurements on the bilateral frontotemporal forehead with a multi-distance device (Brite MKII). We obtain the blood flow velocity in both middle cerebral arteries with TCD and intra-arterial ABP waveforms. End-tidal CO2 and other haemodynamic variables are monitored intermittently. Our primary comparison is TCD+ABP-based DCA (low-frequency phase shift) versus NIRS-based DCA (corrected low-frequency phase shift), before and after cross-clamping of the carotid artery. To investigate the influence of extracerebral tissue (scalp, skull) on the NIRS-measurements, we compare the results of source-detector (SD) pairs at four SD distances (1, 3, 4, 5 cm). Initial results and discussion: To date 15 measurements have been performed. Our initial results show that TCD and NIRS data quality have been variable. Ten measurements showed movement related artifacts and signal loss in TCD. Movement artifacts were present to a variable degree in most NIRS-measurements. ABP-data was of excellent quality. Artifacts were mostly related to movement of the sensors in the surgical process. To allow reliable identification and correction of the artifact segments, we currently explore multivariate signal identification techniques.2, 3 Multivariate techniques should be able to exploit the strong overlap in signal content that is present in physiological artifact-free TCD, NIRS, and ABP data. We will show the initial results on our obtained data. References: 1. Elting JWJ, Tas J, Aries MJH, Czosnyka M, Maurits NM. Dynamic cerebral autoregulation estimates derived from near infrared spectroscopy and transcranial Doppler are similar after correction for transit time and blood flow and blood volume oscillations. J Cereb Blood Flow Metab SAGE Publications Sage UK: London, England; 2020; 40: 135–49 2. Rehman N, Mandic DP. Multivariate empirical mode decomposition. Proc R Soc A Math Phys Eng Sci [Internet] Royal Society; 2010; 466: 1291–302 Available from: https://doi.org/10.1098/rspa.2009.0502 3. Rehman N u., Aftab H. Multivariate Variational Mode Decomposition. IEEE Trans Signal Process 2019; 67: 6039–52