Ocular microtremor during general anesthesia: results of a multicenter trial using automated signal analysis

Mairead Heaney1, Leo G Kevin, Alex R Manara

  • 1Departments of *Anesthesia and ¶Neurosurgery, Beaumont Hospital, Dublin, Ireland; †Department of Anesthesia, Frenchay Hospital, Bristol, United Kingdom; Departments of ‡Neurosurgery and §Anesthesiology, Regional Medical Center, University of Tennessee, Memphis, Tennessee; and ||Division of Neurosurgery, St. Louis University Hospital, St. Louis, Missouri.

Anesthesia and Analgesia
|August 31, 2004
PubMed

Insights

Ocular microtremor (OMT) accurately tracks anesthetic depth and predicts patient responses during surgery. This automated analysis system reliably measures OMT, even with neuromuscular blockade and position changes.

Area of Science:

  • Anesthesiology
  • Neuroscience
  • Biomedical Engineering

Background:

  • Ocular microtremor (OMT) is a physiologic eye tremor linked to brainstem neuronal activity.
  • Previous OMT analysis relied on manual or oscilloscope methods, limiting real-time application.
  • Anesthetics like propofol and sevoflurane affect OMT frequency, suggesting its potential as an anesthetic depth indicator.

Purpose of the Study:

  • To evaluate an automated OMT signal analysis system for accuracy in diverse surgical patients.
  • To assess OMT's ability to identify unconsciousness and predict responses to stimuli during general anesthesia.
  • To investigate the impact of neuromuscular blockade and patient positioning on OMT.

Main Methods:

  • A multicenter trial involving 214 patients undergoing general anesthesia for extracranial surgery.
  • Continuous OMT measurement using a closed-eye piezoelectric technique.
  • Analysis of OMT changes during anesthetic induction, stimulation, and emergence, with assessment of neuromuscular blockade and position effects.

Main Results:

  • OMT decreased upon anesthetic induction and increased during surgical/airway stimulation and emergence.
  • OMT frequency accurately predicted movement in response to airway instrumentation and command at emergence.
  • Neuromuscular blockade reduced OMT amplitude but not frequency; patient position did not affect OMT frequency.

Conclusions:

  • Automated OMT analysis reliably determines anesthetic state in surgical patients.
  • OMT is a valid depth of anesthesia indicator, effective even during neuromuscular blockade and positional changes.
  • This automated system offers a promising tool for real-time anesthetic monitoring.