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Abstract
Existing cerebrovascular blood pressure autoregulation metrics have not been translated to clinical care for pediatric cardiac arrest, in part because signal noise causes high index time-variability. We tested whether a wavelet method that uses near-infrared spectroscopy (NIRS) or intracranial pressure (ICP) decreases index variability compared to that of commonly used correlation indices. We also compared whether the methods identify the optimal arterial blood pressure (ABPopt) and lower limit of autoregulation (LLA). 68 piglets were randomized to cardiac arrest or sham procedure with continuous monitoring of cerebral blood flow using laser Doppler, NIRS and ICP. The arterial blood pressure (ABP) was gradually reduced until it dropped to below the LLA. Several autoregulation indices were calculated using correlation and wavelet methods, including the pressure reactivity index (PRx and wPRx), cerebral oximetry index (COx and wCOx), and hemoglobin volume index (HVx and wHVx). Wavelet methodology had less index variability with smaller standard deviations. Both wavelet and correlation methods distinguished functional autoregulation (ABP above LLA) from dysfunctional autoregulation (ABP below the LLA). Both wavelet and correlation methods also identified ABPopt with high agreement. Thus, wavelet methodology using NIRS may offer an accurate vasoreactivity monitoring method with reduced signal noise after pediatric cardiac arrest.
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1 Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311); Department of Physiological Nursing, University of California, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
2 Department of Physiological Nursing, University of California, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); Department of Neurosurgery, School of Medicine, University of California, Los Angeles, USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718); Department of Neurological Surgery, University of California, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811); Institute of Computational Health Sciences, University of California, San Francisco, USA (GRID:grid.266102.1) (ISNI:0000 0001 2297 6811)
3 Northwestern University, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Anesthesiology, Chicago, USA (GRID:grid.413808.6) (ISNI:0000 0004 0388 2248)
4 Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311)
5 Brain Physics Laboratory, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK (GRID:grid.120073.7) (ISNI:0000 0004 0622 5016)
6 Brain Physics Laboratory, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK (GRID:grid.120073.7) (ISNI:0000 0004 0622 5016); Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland (GRID:grid.1035.7) (ISNI:0000000099214842)
7 Brain Physics Laboratory, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK (GRID:grid.120073.7) (ISNI:0000 0004 0622 5016); Department of Anaesthesiology, University of Auckland, Auckland, New Zealand (GRID:grid.9654.e) (ISNI:0000 0004 0372 3343)
8 Department of Anesthesiology and Critical Care Medicine, Division of Pediatric Anesthesiology, Johns Hopkins University, Baltimore, USA (GRID:grid.21107.35) (ISNI:0000 0001 2171 9311)