Automated Pupillometry for Assessment of Treatment Success in Nonconvulsive Status Epilepticus

Quantitative Pupillometry: Clinical Applications for the Internist

From the time of Galen, examining of the pupillary light reflex has been a standard of care across the continuum of healthcare. The growing body of evidence overwhelmingly supports the use of quantitative pupillometry over subject examination with flashlight or penlight. At current time, pupillometers have become standard-of-care in many hospitals across six continents. This review paper provides an overview and rationale for pupillometer use and highlights literature supporting pupillometer derived measures of the pupillary light reflex in both neurological and non-neurological patients across the healthcare continuum.

Prediction for the prognosis of diffuse axonal injury using automated pupillometry

OBJECTIVE

Previous studies have reported various predictive indicators of diffuse axonal injury (DAI), but no consensus has not been reached. Although the efficiency of automated pupillometry in patients with consciousness disorder has been widely reported, there are few reports of its use in patients with DAI. This study aimed to investigate the significance of pupillary findings in predicting the prognosis of DAI.

PATIENTS AND METHODS

We included patients admitted to our center with a diagnosis of DAI from June 1, 2021 to June 30, 2022. Pupillary findings in both eyes were quantitatively measured by automated pupillometry every 2 hours after admission. We statistically examined the correlations between automated pupillometry parameters, the patients' characteristics, and outcomes such as the Glasgow Outcome Scale Extended (GOSE) after 6 months from injury, the time to follow command, and so on.

RESULTS

Among 22 patients included in this study, five had oculomotor nerve palsy. Oculomotor nerve palsy was correlated with all outcomes, whereas Marshall computed tomography (CT) classification, Injury severity score (ISS) and DAI grade were correlated with few outcomes. Some of the automated pupillometry parameters were significantly correlated with GOSE at 6 months after injury, and many during the first 24 hours of measurement were correlated with the time to follow command. Most of these results were not affected by adjustment using sedation period, ISS or Marshall CT classification. A subgroup analysis of patients without oculomotor nerve palsy revealed that many of the automated pupillometry parameters during the first 24 hours of measurement were significantly correlated with most of the outcomes. The cutoff values that differentiated a good prognosis (GOSE 5-8) from a poor prognosis (GOSE 1-4) were constriction velocity (CV) 1.43 (AUC = 0.81(0.62-1), p = 0.037) and maximum constriction velocity (MCV) 2.345 (AUC = 0.78 (0.58-0.98), p = 0.04). The cutoff values that differentiated the time to follow command into within 7 days and over 8 days were percentage of constriction 8 (AUC = 0.89 (0.68-1), p = 0.011), CV 0.63 (AUC = 0.92 (0.78-1), p = 0.013), MCV 0.855 (AUC = 0.9 (0.74-1), p = 0.017) and average dilation velocity 0.175 (AUC = 0.95 (0.86-1), p = 0.018).

CONCLUSIONS

The present results indicate that pupillary findings in DAI are a strong predictive indicator of the prognosis, and that quantitative measurement of them using automated pupillometry could facilitate enhanced prediction for the prognosis of DAI.

Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW

We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury.

RECENT FINDINGS

Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms.

SUMMARY

The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

The Neurological Pupil index for outcome prognostication in people with acute brain injury (ORANGE): a prospective, observational, multicentre cohort study

BACKGROUND

Improving the prognostication of acute brain injury is a key element of critical care. Standard assessment includes pupillary light reactivity testing with a hand-held light source, but findings are interpreted subjectively; automated pupillometry might be more precise and reproducible. We aimed to assess the association of the Neurological Pupil index (NPi)-a quantitative measure of pupillary reactivity computed by automated pupillometry-with outcomes of patients with severe non-anoxic acute brain injury.

METHODS

ORANGE is a multicentre, prospective, observational cohort study at 13 hospitals in eight countries in Europe and North America. Patients admitted to the intensive care unit after traumatic brain injury, aneurysmal subarachnoid haemorrhage, or intracerebral haemorrhage were eligible for the study. Patients underwent automated infrared pupillometry assessment every 4 h during the first 7 days after admission to compute NPi, with values ranging from 0 to 5 (with abnormal NPi being <3). The co-primary outcomes of the study were neurological outcome (assessed with the extended Glasgow Outcome Scale [GOSE]) and mortality at 6 months. We used logistic regression to model the association between NPi and poor neurological outcome (GOSE ≤4) at 6 months and Cox regression to model the relation of NPi with 6-month mortality. This study is registered with ClinicalTrials.gov, NCT04490005.

FINDINGS

Between Nov 1, 2020, and May 3, 2022, 514 patients (224 with traumatic brain injury, 139 with aneurysmal subarachnoid haemorrhage, and 151 with intracerebral haemorrhage) were enrolled. The median age of patients was 61 years (IQR 46-71), and the median Glasgow Coma Scale score on admission was 8 (5-11). 40 071 NPi measurements were taken (median 40 per patient [20-50]). The 6-month outcome was assessed in 497 (97%) patients, of whom 160 (32%) patients died, and 241 (47%) patients had at least one recording of abnormal NPi, which was associated with poor neurological outcome (for each 10% increase in the frequency of abnormal NPi, adjusted odds ratio 1·42 [95% CI 1·27-1·64]; p<0·0001) and in-hospital mortality (adjusted hazard ratio 5·58 [95% CI 3·92-7·95]; p<0·0001).

INTERPRETATION

NPi has clinically and statistically significant prognostic value for neurological outcome and mortality after acute brain injury. Simple, automatic, repeat automated pupillometry assessment could improve the continuous monitoring of disease progression and the dynamics of outcome prediction at the bedside.

FUNDING

NeurOptics.

Application of Pupillometry in Neurocritical Patients

Pupillary light reflex (PLR) assessment is a crucial examination for evaluating brainstem function, particularly in patients with acute brain injury and neurosurgical conditions. The PLR is controlled by neural pathways modulated by both the sympathetic and parasympathetic nervous systems. Altered PLR is a strong predictor of adverse outcomes after traumatic and ischemic brain injuries. However, the assessment of PLR needs to take many factors into account since it can be modulated by various medications, alcohol consumption, and neurodegenerative diseases. The development of devices capable of measuring pupil size and assessing PLR quantitatively has revolutionized the non-invasive neurological examination. Automated pupillometry, which is more accurate and precise, is widely used in diverse clinical situations. This review presents our current understanding of the anatomical and physiological basis of the PLR and the application of automated pupillometry in managing neurocritical patients. We also discuss new technologies that are being developed, such as smartphone-based pupillometry devices, which are particularly beneficial in low-resource settings.

Neuromonitoring in Critically Ill Patients

OBJECTIVES

Critically ill patients are at high risk of acute brain injury. Bedside multimodality neuromonitoring techniques can provide a direct assessment of physiologic interactions between systemic derangements and intracranial processes and offer the potential for early detection of neurologic deterioration before clinically manifest signs occur. Neuromonitoring provides measurable parameters of new or evolving brain injury that can be used as a target for investigating various therapeutic interventions, monitoring treatment responses, and testing clinical paradigms that could reduce secondary brain injury and improve clinical outcomes. Further investigations may also reveal neuromonitoring markers that can assist in neuroprognostication. We provide an up-to-date summary of clinical applications, risks, benefits, and challenges of various invasive and noninvasive neuromonitoring modalities.

DATA SOURCES

English articles were retrieved using pertinent search terms related to invasive and noninvasive neuromonitoring techniques in PubMed and CINAHL.

STUDY SELECTION

Original research, review articles, commentaries, and guidelines.

DATA EXTRACTION

Syntheses of data retrieved from relevant publications are summarized into a narrative review.

DATA SYNTHESIS

A cascade of cerebral and systemic pathophysiological processes can compound neuronal damage in critically ill patients. Numerous neuromonitoring modalities and their clinical applications have been investigated in critically ill patients that monitor a range of neurologic physiologic processes, including clinical neurologic assessments, electrophysiology tests, cerebral blood flow, substrate delivery, substrate utilization, and cellular metabolism. Most studies in neuromonitoring have focused on traumatic brain injury, with a paucity of data on other clinical types of acute brain injury. We provide a concise summary of the most commonly used invasive and noninvasive neuromonitoring techniques, their associated risks, their bedside clinical application, and the implications of common findings to guide evaluation and management of critically ill patients.

CONCLUSIONS

Neuromonitoring techniques provide an essential tool to facilitate early detection and treatment of acute brain injury in critical care. Awareness of the nuances of their use and clinical applications can empower the intensive care team with tools to potentially reduce the burden of neurologic morbidity in critically ill patients.

Predicting Coma Trajectories: The Impact of Bias and Noise on Shared Decisions

Coma trajectories are characterized by quick awakening or protracted awakening. Outcome is bookended by restored functionality or permanent cognitively and physically debilitated states. Given the stakes, prognostication cannot be easily questioned as a judgment call, and a scientific underpinning is elemental. Conventional wisdom in determining coma-outcome trajectories posits that (1) predictive models are better than personal experiences, (2) self-fulfilling prophesy is unchecked and driven by nihilism, with little regard for prior probability outcomes, and (3) recovery is impacted by patients’ prior wishes and preexisting medical conditions—but also by what families are told about the patient’s state and anticipated clinical course. Moreover, a predicted good outcome can be offset by a major subsequent complication, or a predicted poor outcome can be offset by aggressive care. This article examines some of these concepts, including how we decide on aggressiveness of care, how we judge quality of life, and the impact on outcome. Most patients who awaken quickly do well and can resume their pretrauma injury lives. In worse off, slow-to-awaken patients, outcomes are a mixed bag of limited innate resilience, depleted cognitive and physical reserves, and adjusted quality of life. Bias and noise are factors not easily measured in outcome prediction, but their influence on recovery trajectories raises some troubling issues.