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

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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON54402

Enrollment

240

Registered

2021-01-18

Start date

2021-01-22

Completion date

Unknown

Last updated

2024-11-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cerebrale autoregulatie onder algehele narcose Cerebral autoregulation control mechanisms of brain blood flow

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: - >=18 years of age - Informed consent - Planned for any type of elective surgery/Requiring intubation/Requiring tracheostomy

Exclusion criteria

Exclusion criteria: - Any right-sided structural pathology or reduced function (Tapse

Design outcomes

Primary

Measure	Time frame
The primary aim of this study is data collection of continuous noninvasive arterial pressure waveform signals with the 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, transcranial Doppler ultrasound, capnography and clinical data from patients EMR in surgical patients. These data will be used to predict the likelihood of derangement of physiologic parameters in awake patients before induction of anesthesia and to predict cerebral blood flow using machine learning.	—

Measure

Time frame

The primary aim of this study is data collection of continuous noninvasive arterial pressure waveform signals with the 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, transcranial Doppler ultrasound, capnography and clinical data from patients EMR in surgical patients. These data will be used to predict the likelihood of derangement of physiologic parameters in awake patients before induction of anesthesia and to predict cerebral blood flow using machine learning.

—

Secondary

Measure	Time frame
Not applicable.	—

Countries

Netherlands

Outcome results