Prediction of cerebral blood flow and perfusion with arterial pulse wave applied machine learning

Intra-operative hypotension, even in short periods, is associated with
increased mortality, the occurrence of postoperative renal failure, myocardial
injury and length of hospital stay. Also the incidence of ischemic stroke is
slightly elevated. It is hypothesized that when intra-operative blood pressure
declines, cerebral autoregulation (CA), known to keep cerebral blood flow
stable during blood pressure fluctuations, becomes impaired and that brain
perfusion becomes jeopardized. Real-time assessment of cerebral autoregulation
requires extensive, specialized monitoring and complicated data processing, and
is not routinely performed.
Therefore, in daily practice, anesthesiologists strive to maintain mean blood
pressure above approximately 60 to 65 mmHg since studies claim that the lower
limit of CA is located around this blood pressure level. However, it is
becoming increasingly clear that there is a large inter-individual variation in
lower limit of CA, and we underestimate the risk of intraoperative cerebral
hypoperfusion. In this study we want to collect the beat-to-beat arterial blood
pressure curve, cerebral blood flow velocity and cerebral tissue oxygenation to
try to predict the cerebral perfusion from the arterial pulse wave.

The primary aim of this study is data collection of continuous noninvasive
arterial pressure waveform signals with the ClearSight (CS) finger cuff,
continuous invasive arterial pressure waveform signals when an arterial cannula
is already available due to standard of care, continuous noninvasive cerebral
oximetry signals, cerebral flow velocity using transcranial Doppler (TCD)
ultrasound and clinical data from patients* electronic medical record (EMR) in
surgical patients. These data will serve as a base to attempt to predict
cerebral perfusion during surgery using hemodynamic parameters only by use of
machine-learning.
The collected digital pressure waveform data will be used to assess the
feasibility, the learning and building of an initial ML model using the
CS/EV1000/HemoSphere continuous noninvasive arterial pressure signal and
internally validate it.

This is a non-randomized prospective observational data collection study. We
will start to monitor beat-to-beat systemic and cerebral hemodynamic parameters
(non-invasive finger blood pressure (photoplethysmograph), non-invasive middle
cerebral artery blood flow velocity (transcranial Doppler) and non-invasive
cerebral tissue oxygenation (Near infra-red spectroscopy) from until at least
30 minutes before the start of surgery until the procedure ends.
We aim to include 100 patients for cardiac and non-cardiac surgery.

There are no additional risks or benefits associated with participation. There
are no investigational devices used in this study. There are no additional
risks associated with the use of the CS/EV1000/HemoSphere monitor other than
described in the Instructions for Use. There are also no risks associated with
the study procedures. The anesthetic regimes are based on what is currently
used in daily practice and reported in the literature. Patients receive
standard anesthesia on basis of daily practice and established pharmacodynamic
models that have been shown to be both save and effective.