Comparison of optical measurements of critical closing pressure acquired before and during induced ventricular arrhythmia in adults.
Alec LafontantElizabeth Mahanna-GabrielliJoseph P CulverRodrigo Menezes FortiTiffany S KoRonak M ShahJeffrey S ArklesDaniel J LichtArjun G YodhW Andrew KofkeBrian R WhiteWesley Boehs BakerPublished in: Neurophotonics (2022)
Significance: The critical closing pressure (CrCP) of cerebral circulation, as measured by diffuse correlation spectroscopy (DCS), is a promising biomarker of intracranial hypertension. However, CrCP techniques using DCS have not been assessed in gold standard experiments. Aim: CrCP is typically calculated by examining the variation of cerebral blood flow (CBF) during the cardiac cycle (with normal sinus rhythm). We compare this typical CrCP measurement with a gold standard obtained during the drops in arterial blood pressure (ABP) caused by rapid ventricular pacing (RVP) in patients undergoing invasive electrophysiologic procedures. Approach: Adults receiving electrophysiology procedures with planned ablation were enrolled for DCS CBF monitoring. CrCP was calculated from CBF and ABP data by three methods: (1) linear extrapolation of data during RVP ( CrCP RVP ; the gold standard); (2) linear extrapolation of data during regular heartbeats ( CrCP Linear ); and (3) fundamental harmonic Fourier filtering of data during regular heartbeats ( CrCP Fourier ). Results: CBF monitoring was performed prior to and during 55 episodes of RVP in five adults. CrCP RVP and CrCP Fourier demonstrated agreement ( R = 0.66 , slope = 1.05 (95%CI, 0.72 to 1.38). Agreement between CrCP RVP and CrCP Linear was worse; CrCP Linear was 8.2 ± 5.9 mmHg higher than CrCP RVP (mean ± SD; p < 0.001 ). Conclusions: Our results suggest that DCS-measured CrCP can be accurately acquired during normal sinus rhythm.
Keyphrases
- blood pressure
- electronic health record
- big data
- patients undergoing
- cerebral blood flow
- left ventricular
- heart rate
- catheter ablation
- type diabetes
- machine learning
- subarachnoid hemorrhage
- single molecule
- adipose tissue
- data analysis
- insulin resistance
- brain injury
- artificial intelligence
- deep learning
- skeletal muscle
- optical coherence tomography
- sensitive detection