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Updated: Jan 21, 2026
06:04
Objective Nociceptive Assessment in Ventilated ICU Patients: A Feasibility Study Using Pupillometry and the Nociceptive Flexion Reflex
Published on: July 4, 2018
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Correlation Between Electroencephalography and Automated Pupillometry in Critically Ill Patients: A Pilot Study
Sania Hasan1,2, Lorenzo Peluso1, Lorenzo Ferlini2
1Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles.
Journal of Neurosurgical Anesthesiology
|July 26, 2019
Summary
Automated pupillometry shows correlation with EEG patterns in critically ill patients. This method aids in assessing cerebral dysfunction, offering a valuable tool for monitoring neurological status.
Area of Science:
- Neuroscience
- Critical Care Medicine
- Neurology
Background:
- Electroencephalography (EEG) is crucial for monitoring critically ill comatose patients, but interpretation challenges exist.
- Evaluating the correlation between EEG background patterns/reactivity and automated pupillometry is essential for improved patient assessment.
Purpose of the Study:
- To investigate the correlation between EEG background patterns and reactivity to stimuli with automated pupillometry in critically ill patients.
Main Methods:
- Prospective study involving 60 adult patients monitored with continuous EEG and automated pupillometry.
- Pupillary changes to light stimulation were assessed using NeuroLight Algiscan.
- EEG encephalopathy and reactivity were scored by blinded neurophysiologists; pupillary metrics (size, constriction, velocity, latency) were collected.
Main Results:
- Significant differences in pupillary size, constriction rate, and velocity were observed across EEG encephalopathy categories.
- Reactive EEG tracings correlated with greater pupil size, constriction rate, and velocity compared to nonreactive ones.
- Pupillary constriction rate predicted severe encephalopathy with 85% sensitivity and 79% specificity (AUC 0.83).
Conclusions:
- Automated pupillometry provides valuable data for assessing cerebral dysfunction in critically ill patients.
- This technology can complement EEG in monitoring neurological status and guiding clinical decisions.
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