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Clinical Trials/NCT06480162
NCT06480162
Recruiting
Not Applicable

Impact of Mechanical Ventilation on Hippocampal Oscillation and Respiratory-hippocampus Coupling

Beijing Sanbo Brain Hospital1 site in 1 country20 target enrollmentMay 8, 2024

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Mechanical Ventilation Complication
Sponsor
Beijing Sanbo Brain Hospital
Enrollment
20
Locations
1
Primary Endpoint
intracranial EEG (iEEG) recording of the hippocampus
Status
Recruiting
Last Updated
last year

Overview

Brief Summary

Ventilator-associated brain injury (VABI) frequently occurs in ICU patients. Although animal studies indicate the hippocampus as a key target of VABI, its relevance in human patients remains uncertain. In this study, the investigators aim to monitor hippocampal EEG activity during the weaning process of patients off mechanical ventilation, and also exploring the coupling between breathing patterns and hippocampal activity.

Detailed Description

\[Rational\] Although mechanical ventilation (MV) is crucial for saving lives, the high incidence of ventilator-associated brain injury (VABI) in ICU patients, affecting up to 80% of cases, raises significant clinical concerns. VABI not only increases mortality and morbidity rates but also strains healthcare resources. Despite abundant evidence from preclinical studies, the precise impact of mechanical ventilation on humans remains largely unknown. Understanding these effects is crucial for uncovering the mechanisms behind VABI and subsequently preventing and treating this complication. \[Methods\] Patients with drug-resistant epilepsy scheduled for stereoelectroencephalography (sEEG) exploration as part of their pre-surgical assessment will undergo eligibility screening. During the study, sEEG signals and respiratory data will be simultaneously collected from the hippocampus and other relevant limbic structures under various mechanical ventilation scenarios. These scenarios include controlled ventilation at different levels of positive end-expiratory pressure (PEEP, 5cmH2O vs. 10cmH2O) and respiratory rates (RR, 10 vs. 20 bpm), assisted ventilation, and natural breathing phases. Following the discontinuation of mechanical ventilation, patients will be monitored for up to 7 days to evaluate the occurrence of delirium. Additionally, simultaneous intracranial EEG recordings from relevant cortical and subcortical regions will be conducted. \[Aims\] The current study aims to investigate the effects of mechanical ventilation, including variations in RR and PEEP, on neurophysiological electrical oscillations within the hippocampus, as well as the relationship between breathing patterns and hippocampal activity. Furthermore, the investigators will explore the impact of mechanical ventilation on other cortical areas involved in breathing, depending on electrode positioning, and examine the interactions between these brain regions and the hippocampus.

Registry
clinicaltrials.gov
Start Date
May 8, 2024
End Date
June 1, 2025
Last Updated
last year
Study Type
Observational
Sex
All

Investigators

Sponsor
Beijing Sanbo Brain Hospital
Responsible Party
Principal Investigator
Principal Investigator

Zhonghua Shi, MD, PhD

Deputy president of the department ICU

Beijing Sanbo Brain Hospital

Eligibility Criteria

Inclusion Criteria

  • Age: ≥ 18 years
  • ASA: I - II
  • w/ electrode inside hippocampus (≥ 1 contacts)
  • Singed consent form

Exclusion Criteria

  • Seizure occurrence ≤ 36 hours before op.
  • Structural brain damage
  • History of using: opioids, enzyme-inducing medications, sleep aids, or excessive alcohol consumption
  • History of mechanical ventilation (\>24h)
  • Cognitive impairment
  • Operation within 6 months
  • Participate in other clinical trials in the last four weeks
  • Pregnant or breastfeeding

Outcomes

Primary Outcomes

intracranial EEG (iEEG) recording of the hippocampus

Time Frame: During the process of weaning off mechanical ventilation, assessed up to 3 hours.

The iEEG data from each patient will be analyzed using Python (version 3.12.4). The data will be represented as power spectral density, also known as the power spectrum or spectrum, which measures the frequency distribution of energy or power within a signal. The power spectrum for each channel will be computed using the Fast Fourier Transform (FFT).

Study Sites (1)

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