Correlation Between Electroencephalography and Automated Pupillometry in Critically Ill Patients: A Pilot Study

Abstract

Background: Electroencephalography (EEG) is widely used in the monitoring of critically ill comatose patients, but its interpretation is not straightforward. The aim of this study was to evaluate whether there is a correlation between EEG background pattern/reactivity to stimuli and automated pupillometry in critically ill patients. Methods: Prospective assessment of pupillary changes to light stimulation was obtained using an automated pupillometry (NeuroLight Algiscan, ID-MED, Marseille, France) in 60 adult patients monitored with continuous EEG. The degree of encephalopathy and EEG reactivity were scored by 3 independent neurophysiologists blinded to the patient's history. The median values of baseline pupil size, pupillary constriction, constriction velocity, and latency were collected for both eyes. To assess sensitivity and specificity, we calculated areas under the receiver-operating characteristic curve. Results: The degree of encephalopathy assessed by EEG was categorized as mild (42%), moderate (37%), severe (10%) or suppression-burst/suppression (12%); a total of 47/60 EEG recordings were classified as "reactive." There was a significant difference in pupillary size, constriction rate, and constriction velocity, but not latency, among the different EEG categories of encephalopathy. Similarly, reactive EEG tracings were associated with greater pupil size, pupillary constriction rate, and constriction velocity compared with nonreactive recordings; there were no significant differences in latency. Pupillary constriction rate values had an area under the curve of 0.83 to predict the presence of severe encephalopathy or suppression-burst/suppression, with a pupillary constriction rate of < 20% having a sensitivity of 85% and a specificity of 79%. Conclusions: Automated pupillometry can contribute to the assessment of cerebral dysfunction in critically ill patients.

Quantitative Assessments of Pupillary Light Reflexes in Hospital-Onset Unconsciousness

“…Autonomic dysfunction mediated by in ammatory response likely has an important role in the pathogenesis of the dysfunction of the brain or other organs [36]. Neurotransmitter imbalance such as cholinergic de ciency due to in ammation and multiorgan dysfunction in critically ill patients can lead to pupillary dysfunction [18,33]. In this context, although there is limited evidence on the association between QP values and the severity of encephalopathy [37], QP as an indicator of autonomic and brain dysfunction may play a role in assessing the severity of metabolic encephalopathy as the cause of HOU (Fig.…”

“…Moreover, QP was shown to be useful in neurological prognostication in unconscious patients after cardiac arrest [11][12][13][14][15][16][17]. Because the PLR is regulated by the autonomic nervous system, QP may also have a role in the assessment of autonomic dysfunction in critically ill patients [18,19]. However, studies on the QP ndings in patients who are hospitalized outside intensive care units are lacking.…”

How to monitor the brain in COVID-19 patients?

“…Automated pupillometry is a bedside technique able to precisely quantify the pupillary size and pupillary light reflex (PLR). Change in pupillary activity showed a good accuracy to predict delirium in ventilated critically ill patients and to detect unreactive EEG background ( Favre et al, 2020 , Hasan et al, 2019 ). As such, automated pupillometry could be also used to titrate the administration of anaesthetics or analgesics, as excessive drug regimens would provide reduced PLR or size, respectively, or identify patients at risk of delirium (i.e.…”

Automated Pupillometry for Prediction of Electroencephalographic Reactivity in Critically Ill Patients: A Prospective Cohort Study

“…A pilot study showed that the reduction in pupillary constriction rate to light stimulation derived from automated pupillometry identified patients “at risk” of an unreactive background EEG ( 12 ). However, this study had several limitations, including the small sample size, lack of adjustment for several confounders, e.g., use of sedatives or underlying brain injury, and lack of additional automated pupillometry-derived variables, such as the Neurological Pupil index (NPi), which has been shown to predict neurological outcome in critically ill patients ( 13 , 14 ).…”