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.
Original language | English (US) |
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Article number | 5926 |
Journal | Scientific reports |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2020 |
Funding
We thank Ms. Claire Levine, MS, ESL for her editing skills. This study was supported by funding from the National Institutes of Health (NIH) grants R01 NS107417 (J.K.L.), K08 NS080984 (J.K.L.), R01 NS060703 (RCK), R21 NS095036 (RCK), R01 HL139543 (RCK), R01 NS076738 (X.H.), and NS106905A1 (X.H.); the American Heart Association Transformational Project Award (J.K.L.), and Middle Career Scientist Award, UCSF Institute for Computational Health Sciences (X.H.). M.C. is supported by National Institute of Health Research, BRC, MIC, Cambridge, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ASJC Scopus subject areas
- General