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Published on: October 16, 2013
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.
Abstract:
Ocular microtremor (OMT) is a fine physiologic tremor of the eye related to neuronal activity in the reticular formation of the brainstem. The frequency of OMT is suppressed by propofol and sevoflurane and predicts the response to command at emergence from anesthesia. Previous studies have relied on post hoc computer analysis of OMT wave forms or on real-time measurements confirmed visually on an oscilloscope. Our overall aim was to evaluate an automated system of OMT signal analysis in a diverse patient population undergoing general anesthesia. In a multicenter trial involving four centers in three countries, we examined the accuracy of OMT to identify the unconscious state and to predict movement in response to airway instrumentation and surgical stimulation. We also tested the effects of neuromuscular blockade and patient position on OMT. We measured OMT continuously by using the closed-eye piezoelectric technique in 214 patients undergoing extracranial surgery with general anesthesia using a variety of anesthetics. OMT decreased at induction in all patients, increased transiently in response to surgical incision or airway instrumentation, and increased at emergence. The frequency of OMT predicted movement in response to laryngeal mask airway insertion and response to command at emergence. Neuromuscular blockade did not affect the frequency of OMT but decreased its amplitude. OMT frequency was unaffected by changes in patient position. We conclude that OMT, measured by an automated signal analysis module, accurately determines the anesthetic state in surgical patients, even during profound neuromuscular blockade and after changes in patient position.
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.
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