Quantitative Pupillometry in the Intensive Care Unit

Smartphone pupillometry for detection of cerebral vasospasm after aneurysmal subarachnoid hemorrhage

OBJECTIVES

Vasospasm is a complication of aneurysmal subarachnoid hemorrhage (aSAH) that can change the trajectory of recovery and is associated with morbidity and mortality. Earlier detection of vasospasm could improve patient outcomes. Our objective is to evaluate the accuracy of smartphone-based quantitative pupillometry in the detection of radiographic vasospasm and delayed cerebral ischemia (DCI) after aSAH.

MATERIALS AND METHODS

We prospectively collected pupillary light reflex (PLR) parameters from patients with aSAH admitted to a neurocritical care unit at a single hospital twice daily using quantitative smartphone pupillometry recordings. PLR parameters included: Maximum pupil diameter, minimum pupil diameter, percent change in pupil diameter, latency in beginning of pupil constriction to light, mean constriction velocity, maximum constriction velocity, and mean dilation velocity. Two-tailed t-tests for independent samples were performed to determine changes in average concurrent PLR parameter values between the following comparisons: (1) patients with and without radiographic vasospasm (defined by angiography with the need for endovascular intervention) and (2) patients with and without DCI.

RESULTS

49 subjects with aSAH underwent 323 total PLR recordings. For PLR recordings taken with (n=35) and without (n=241) radiographic vasospasm, significant differences were observed in MIN (35.0 ± 7.5 pixels with vasospasm versus 31.6 ± 6.2 pixels without; p=0.002). For PLR recordings taken with (n=43) and without (n=241) DCI, significant differences were observed in MAX (48.9 ± 14.3 pixels with DCI versus 42.5 ± 9.2 pixels without; p<0.001).

CONCLUSIONS

Quantitative smartphone pupillometry has the potential to be used to detect radiographic vasospasm and DCI after aSAH.

Noninvasive Intracranial Pressure Monitoring: Are We There Yet?

There is an urgent unmet need for a reliable noninvasive tool to detect elevations in intracranial pressure (ICP) above guideline-recommended thresholds for treatment. Gold standard invasive ICP monitoring is unavailable in many settings, including resource-limited environments, and in situations such as liver failure in which coagulopathy increases the risk of invasive monitoring. Although a large number of noninvasive techniques have been evaluated, this article reviews the potential clinical role, if any, of the techniques that have undergone the most extensive evaluation and are already in clinical use. Elevations in ICP transmitted through the subarachnoid space result in distension of the optic nerve sheath. The optic nerve sheath diameter (ONSD) can be measured with ultrasound, and an ONSD threshold can be used to detect elevated ICP. Although many studies suggest this technique accurately detects elevated ICP, there is concern for risk of bias and variations in ONSD thresholds across studies that preclude routine use of this technique in clinical practice. Multiple transcranial Doppler techniques have been used to assess ICP, but the best studied are the pulsatility index and the Czosnyka method to estimate cerebral perfusion pressure and ICP. Although there is inconsistency in the literature, recent prospective studies, including an international multicenter study, suggest the estimated ICP technique has a high negative predictive value (> 95%) but a poor positive predictive value (≤ 30%). Quantitative pupillometry is a sensitive and objective method to assess pupillary size and reactivity. Proprietary indices have been developed to quantify the pupillary light response. Limited data suggest these quantitative measurements may be useful for the early detection of ICP elevation. No current noninvasive technology can replace invasive ICP monitoring. Where ICP monitoring is unavailable, multimodal noninvasive assessment may be useful. Further innovation and research are required to develop a reliable, continuous technique of noninvasive ICP assessment.

Nursing insights on the effectiveness of automated pupillometry in two distinct pediatric intensive care units

PURPOSE

Automated pupillometry (AP) facilitates objective pupillary assessment. In this study, we aimed at assessing nursing perspective about the utility of AP in neurocritically ill children to understand acceptance and usage barriers to guide development of a standardized use protocol.

METHODS

We conducted a web-based, cross-sectional, anonymous, Google™ survey of nurses at two independent pediatric ICUs which have been using AP over last four years. The survey included questions related to user-friendliness, barriers, acceptance, frequency of use, and method of documenting AP findings.

RESULTS

A total of 31 nurses responded to the survey. A total of 25 nurses (80.6%) used the automated pupillometer and 19 (61.3%) nurses preferred to use the automated pupillometer on critically ill intubated patients. Respondents rated the pupillometer a median [IQR] frequency of use of 7/10 [4-9] and a mean user-friendliness of 8/10 [7-10]. Barriers to pupillometer use included pupillometer unavailability, technical issues, lack of perceived clinical significance, and infection control.

CONCLUSION

Nurses have widely adopted the use of automated pupillometer in the PICU especially for critically ill intubated patients and rate it favorably for user-friendliness. Barriers against its use include limited resources, infection concerns, technical issues, and a lack of perceived clinical significance and training. Implementation of standardized PICU protocol for AP usage in critically ill children, may enhance the acceptance, increase usage and aid in objective assessments.

PRACTICE IMPLICATIONS

These findings can be used to create a standardized protocol on implementing automated pupillometry in the PICU for critically ill children.

Touchless short-wave infrared imaging for dynamic rapid pupillometry and gaze estimation in closed eyes

BACKGROUND

Assessments of gaze direction (eye movements), pupil size, and the pupillary light reflex (PLR) are critical for neurological examination and neuroscience research and constitute a powerful tool in diverse clinical settings ranging from critical care through endocrinology and drug addiction to cardiology and psychiatry. However, current bedside pupillometry is typically intermittent, qualitative, manual, and limited to open-eye cases, restricting its use in sleep medicine, anesthesia, and intensive care.

METHODS

We combined short-wave infrared (SWIR, ~0.9-1.7μm) imaging with image processing algorithms to perform rapid (~30 ms) pupillometry and eye tracking behind closed eyelids. Forty-three healthy volunteers participated in two experiments with PLR evoked by visible light stimuli or directing eye movements towards screen targets. Imaging was performed simultaneously on one eye closed, and the other open eye serving as ground truth. Data analysis was performed with a custom approach quantifying changes in brightness around the pupil area or with a deep learning U-NET-based procedure.

RESULTS

Here we show that analysis of SWIR imaging data can successfully measure stimulus-evoked PLR in closed-eye conditions, revealing PLR events in single trials and significant PLRs in nearly all individual subjects, as well as estimating gaze direction. The neural net-based analysis could successfully use closed-eye SWIR data to recreate estimates of open-eye images and assess pupil size.

CONCLUSIONS

Continuous touchless monitoring of rapid dynamics in pupil size and gaze direction through closed eyes paves the way for developing devices with wide-ranging applications, fulfilling long-standing goals in clinical and research fields.

Non-invasive technology for brain monitoring: definition and meaning of the principal parameters for the International PRactice On TEChnology neuro-moniToring group (I-PROTECT)

Technologies for monitoring organ function are rapidly advancing, aiding physicians in the care of patients in both operating rooms (ORs) and intensive care units (ICUs). Some of these emerging, minimally or non-invasive technologies focus on monitoring brain function and ensuring the integrity of its physiology. Generally, the central nervous system is the least monitored system compared to others, such as the respiratory, cardiovascular, and renal systems, even though it is a primary target in most therapeutic strategies. Frequently, the effects of sedatives, hypnotics, and analgesics are entirely unpredictable, especially in critically ill patients with multiple organ failure. This unpredictability exposes them to the risks of inadequate or excessive sedation/hypnosis, potentially leading to complications and long-term negative outcomes. The International PRactice On TEChnology neuro-moniToring group (I-PROTECT), comprised of experts from various fields of clinical neuromonitoring, presents this document with the aim of reviewing and standardizing the primary non-invasive tools for brain monitoring in anesthesia and intensive care practices. The focus is particularly on standardizing the nomenclature of different parameters generated by these tools. The document addresses processed electroencephalography, continuous/quantitative electroencephalography, brain oxygenation through near-infrared spectroscopy, transcranial Doppler, and automated pupillometry. The clinical utility of the key parameters available in each of these tools is summarized and explained. This comprehensive review was conducted by a panel of experts who deliberated on the included topics until a consensus was reached. Images and tables are utilized to clarify and enhance the understanding of the clinical significance of non-invasive neuromonitoring devices within these medical settings.

Anisocoria After Direct Light Stimulus is Associated with Poor Outcomes Following Acute Brain Injury

BACKGROUND

Assessing pupil size and reactivity is the standard of care in neurocritically ill patients. Anisocoria observed in critically ill patients often prompts further investigation and treatment. This study explores anisocoria at rest and after light stimulus determined using quantitative pupillometry as a predictor of discharge modified Rankin Scale (mRS) scores.

METHODS

This analysis includes data from an international registry and includes patients with paired (left and right eye) quantitative pupillometry readings linked to discharge mRS scores. Anisocoria was defined as the absolute difference in pupil size using three common cut points (> 0.5 mm, > 1 mm, and > 2 mm). Nonparametric models were constructed to explore patient outcome using three predictors: the presence of anisocoria at rest (in ambient light); the presence of anisocoria after light stimulus; and persistent anisocoria (present both at rest and after light). The primary outcome was discharge mRS score associated with the presence of anisocoria at rest versus after light stimulus using the three commonly defined cut points.

RESULTS

This analysis included 152,905 paired observations from 6,654 patients with a mean age of 57.0 (standard deviation 17.9) years, and a median hospital stay of 5 (interquartile range 3-12) days. The mean admission Glasgow Coma Scale score was 12.7 (standard deviation 3.5), and the median discharge mRS score was 2 (interquartile range 0-4). The ranges for absolute differences in pupil diameters were 0-5.76 mm at rest and 0-6.84 mm after light. Using an anisocoria cut point of > 0.5 mm, patients with anisocoria after light had worse median mRS scores (2 [interquartile range 0-4]) than patients with anisocoria at rest (1 [interquartile range 0-3]; P < .0001). Patients with persistent anisocoria had worse median mRS scores (3 [interquartile range 1-4]) than those without persistent anisocoria (1 [interquartile range 0-3]; P < .0001). Similar findings were observed using a cut point for anisocoria of > 1 mm and > 2 mm.

CONCLUSIONS

Anisocoria after light is a new biomarker that portends worse outcome than anisocoria at rest. After further validation, anisocoria after light should be considered for inclusion as a reported and trended assessment value.

Update in Pediatric Neurocritical Care: What a Neurologist Caring for Critically Ill Children Needs to Know

Currently nearly one-quarter of admissions to pediatric intensive care units (PICUs) worldwide are for neurocritical care diagnoses that are associated with significant morbidity and mortality. Pediatric neurocritical care is a rapidly evolving field with unique challenges due to not only age-related responses to primary neurologic insults and their treatments but also the rarity of pediatric neurocritical care conditions at any given institution. The structure of pediatric neurocritical care services therefore is most commonly a collaborative model where critical care medicine physicians coordinate care and are supported by a multidisciplinary team of pediatric subspecialists, including neurologists. While pediatric neurocritical care lies at the intersection between critical care and the neurosciences, this narrative review focuses on the most common clinical scenarios encountered by pediatric neurologists as consultants in the PICU and synthesizes the recent evidence, best practices, and ongoing research in these cases. We provide an in-depth review of (1) the evaluation and management of abnormal movements (seizures/status epilepticus and status dystonicus); (2) acute weakness and paralysis (focusing on pediatric stroke and select pediatric neuroimmune conditions); (3) neuromonitoring modalities using a pathophysiology-driven approach; (4) neuroprotective strategies for which there is evidence (e.g., pediatric severe traumatic brain injury, post-cardiac arrest care, and ischemic stroke and hemorrhagic stroke); and (5) best practices for neuroprognostication in pediatric traumatic brain injury, cardiac arrest, and disorders of consciousness, with highlights of the 2023 updates on Brain Death/Death by Neurological Criteria. Our review of the current state of pediatric neurocritical care from the viewpoint of what a pediatric neurologist in the PICU needs to know is intended to improve knowledge for providers at the bedside with the goal of better patient care and outcomes.

Assessment of sedation by automated pupillometry in critically ill patients: a prospective observational study

BACKGROUND

Quantitative measurement of pupil change has not been assessed against the Richmond Agitation and Sedation Scale (RASS) and spectral edge frequency (SEF) during sedation. The aim of this study was to evaluate pupillometry against these measures in sedated critically ill adult patients.

METHODS

In ventilated and sedated patients, pupillary variables were measured by automated pupillometry at each RASS level from -5 to 0 after discontinuation of hypnotics, while processed electroencephalogram variables were displayed continuously and SEF was recorded at each RASS level. Correlations were made between percentage pupillary light reflex (%PLR) and RASS, and between %PLR and SEF. The ability of %PLR to differentiate light sedation (RASS ≥-2), moderate (RASS =-3), and deep sedation (RASS ≤-4) was assessed by areas under receiver operating characteristic (ROC) curves.

RESULTS

A total of 163 paired measurements were recorded in 38 patients. With decreasing sedation depth, median %PLR increased progressively from 20% (interquartile range 17-25%) to 36% (interquartile range 33-40%) (P<0.001). Strong correlations were found between %PLR and RASS (Rho=0.635) and between %PLR and SEF (R=0.641). Area under the curve (AUC) of 0.87 with a %PLR threshold of 28% differentiated moderate/light sedation from deep sedation with sensitivity of 83% and specificity of 83%. An AUC of 0.82 with a threshold of 31% distinguished light sedation from moderate/deep sedation with a sensitivity of 81% and a specificity of 75%.

CONCLUSIONS

Quantitative assessment of %PLR correlates with other indicators of sedation depth in critically ill patients.

Serotonin syndrome treated with cyproheptadine using NPi from a digital pupillometer as a therapeutic indicator: A case report

RATIONALE

Serotonin syndrome is a potentially life-threatening condition resulting from the use of antidepressants, their interactions with other serotonergic medications, or poisoning. It presents with a triad of psychiatric, dysautonomic, and neurological symptoms and is sometimes fatal. While cyproheptadine is a specific treatment option, the optimal duration of its administration remains unclear. The purpose of this report is to quantitatively assess the endpoints of serotonin syndrome treatment. Based on the hypothesis that neurological pupil index (NPi) on a digital pupil recorder would correlate with the severity of the serotonin syndrome, we administered cyproheptadine using NPi as an indicator.

PATIENT CONCERNS

A patient with a history of depression was brought to our hospital after he overdosed on 251 tablets of serotonin and noradrenaline reuptake inhibitors.

DIAGNOSES

On day 3, the patient was diagnosed with serotonin syndrome.

INTERVENTIONS

Cyproheptadine syrup was administered at 4 mg every 4 hours. The NPi of the automated pupillometer was simultaneously measured. On day 5, the NPi exceeded 3.0 cyproheptadine was discontinued.

OUTCOMES

The patient was discharged on day 7.

LESSONS

The lack of considerable improvement during the treatment period suggests that the patient may have improved on his own. In this case, the relationship between NPi and the severity of serotonin syndrome could not be determined.

Machine learning approach for ambient-light-corrected parameters and the Pupil Reactivity (PuRe) score in smartphone-based pupillometry

Introduction

The pupillary light reflex (PLR) is the constriction of the pupil in response to light. The PLR in response to a pulse of light follows a complex waveform that can be characterized by several parameters. It is a sensitive marker of acute neurological deterioration, but is also sensitive to the background illumination in the environment in which it is measured. To detect a pathological change in the PLR, it is therefore necessary to separate the contributions of neuro-ophthalmic factors from ambient illumination. Illumination varies over several orders of magnitude and is difficult to control due to diurnal, seasonal, and location variations.

Methods and results

We assessed the sensitivity of seven PLR parameters to differences in ambient light, using a smartphone-based pupillometer (AI Pupillometer, Solvemed Inc.). Nine subjects underwent 345 measurements in ambient conditions ranging from complete darkness (<5 lx) to bright lighting (≲10,000 lx). Lighting most strongly affected the initial pupil size, constriction amplitude, and velocity. Nonlinear models were fitted to find the correction function that maximally stabilized PLR parameters across different ambient light levels. Next, we demonstrated that the lighting-corrected parameters still discriminated reactive from unreactive pupils. Ten patients underwent PLR testing in an ophthalmology outpatient clinic setting following the administration of tropicamide eye drops, which rendered the pupils unreactive. The parameters corrected for lighting were combined as predictors in a machine learning model to produce a scalar value, the Pupil Reactivity (PuRe) score, which quantifies Pupil Reactivity on a scale 0-5 (0, non-reactive pupil; 0-3, abnormal/"sluggish" response; 3-5, normal/brisk response). The score discriminated unreactive pupils with 100% accuracy and was stable under changes in ambient illumination across four orders of magnitude.

Discussion

This is the first time that a correction method has been proposed to effectively mitigate the confounding influence of ambient light on PLR measurements, which could improve the reliability of pupillometric parameters both in pre-hospital and inpatient care settings. In particular, the PuRe score offers a robust measure of Pupil Reactivity directly applicable to clinical practice. Importantly, the formulae behind the score are openly available for the benefit of the clinical research community.

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.

Evaluation of Quantitative Pupillometry in Acute Postinjury Pediatric Concussion

BACKGROUND

Although millions of children sustain concussions each year, a rapid and objective test for concussion has remained elusive. The aim of this study was to investigate quantitative pupillometry in pediatric patients in the acute, postinjury setting.

METHODS

This was a prospective case-control study of concussed patients presenting to the emergency department within 72 hours of injury. Pupillary measurements were gathered using NeurOptics' PLR 3000; evaluation included a symptom checklist and neurocognitive assessment. Data were analyzed using descriptive statistics and regression models.

RESULTS

A total of 126 participants were enrolled. One significant difference in pupillometry between concussed and control participants was found: left minimum pupil diameter in 12- to 18 year-olds (P = 0.02). Models demonstrating odds of a concussion revealed significant associations for time to 75% recovery (T75) of the left pupil in five- to 11-year-olds and average dilation velocity of the left pupil in 12- to 18-year-olds (P = 0.03 and 0.02 respectively). Models predicting symptom improvement showed one significant association: percent change of the right pupil in five-to-11-year-olds (P = 0.02). Models predicting neurocognitive improvement in 12- to 18-year-olds demonstrated significant association in T75 in the left pupil for visual memory, visual motor processing speed, and reaction time (P = 0.002, P = 0.04, P = 0.04).

CONCLUSIONS

The limited statistically significant associations found in this study suggest that pupillometry may not be useful in pediatrics in the acute postinjury setting for either the diagnosis of concussion or to stratify risk for prolonged recovery.

Neuromonitoring of Pediatric and Adult Extracorporeal Membrane Oxygenation Patients: The Importance of Continuous Bedside Tools in Driving Neuroprotective Clinical Care

Extracorporeal membrane oxygenation (ECMO) is a form of temporary cardiopulmonary bypass for patients with acute respiratory or cardiac failure refractory to conventional therapy. Its usage has become increasingly widespread and while reported survival after ECMO has increased in the past 25 years, the incidence of neurological injury has not declined, leading to the pressing question of how to improve time-to-detection and diagnosis of neurological injury. The neurological status of patients on ECMO is clinically difficult to evaluate due to multiple factors including illness, sedation, and pharmacological paralysis. Thus, increasing attention has been focused on developing tools and techniques to measure and monitor the brain of ECMO patients to identify dynamic risk factors and monitor patients' neurophysiological state as a function in time. Such tools may guide neuroprotective interventions and thus prevent or mitigate brain injury. Current means to continuously monitor and prevent neurological injury in ECMO patients are rather limited; most techniques provide indirect or postinsult recognition of irreversible brain injury. This review will explore the indications, advantages, and disadvantages of standard-of-care, emerging, and investigational technologies for neurological monitoring on ECMO, focusing on bedside techniques that provide continuous assessment of neurological health.

Noninvasive Neuromonitoring Modalities in Children Part I: Pupillometry, Near-Infrared Spectroscopy, and Transcranial Doppler Ultrasonography

BACKGROUND

Noninvasive neuromonitoring in critically ill children includes multiple modalities that all intend to improve our understanding of acute and ongoing brain injury.

METHODS

In this article, we review basic methods and devices, applications in clinical care and research, and explore potential future directions for three noninvasive neuromonitoring modalities in the pediatric intensive care unit: automated pupillometry, near-infrared spectroscopy, and transcranial Doppler ultrasonography.

RESULTS

All three technologies are noninvasive, portable, and easily repeatable to allow for serial measurements and trending of data over time. However, a paucity of high-quality data supporting the clinical utility of any of these technologies in critically ill children is currently a major limitation to their widespread application in the pediatric intensive care unit.

CONCLUSIONS

Future prospective multicenter work addressing major knowledge gaps is necessary to advance the field of pediatric noninvasive neuromonitoring.

Overlooked evidence for transmission deficit of pupillary light reflex can be secondary to trigeminal nerve ganglion degeneration following subarachnoid hemorrhage; preliminary experimental study

OBJECTIVE

Although the effect of oculomotor and cervical sympathetic networks on pupil diameter is well known; the effect of the trigeminal nerve on pupil diameter has not been investigated yet. This subject was investigated.

MATERIALS AND METHODS

Five of 23 rabbits were used as a control group (GI; n = 5); 0.5 ccs saline solution into cisterna magna injected animals used as SHAM (GII; n = 5); autologous blood injected to produce SAH used as the study group (GIII; n = 13) and followed up three weeks. Light-stimulated pupil diameters were measured with an ocular tomography device before, middle, and at the end of the experiment. Considering the sclera area/pupil area ratio index (PRI) as the pupillary reaction area, we used this equation for the pupil's rush to light. Degenerated neuron densities of trigeminal ganglia and pupil diameters compared with the Mann-Whitney U test.

RESULTS

The PRI, degenerated neuron density of trigeminal ganglia (n/mm3) were: (2.034 ± 0.301)/(13 ± 3) in GI; (1.678 ± 0.211)/(46 ± 9) in GII; and (0.941 ± 0.136)/(112 ± 21) in GIII. P-values between groups as: p < 0.005 in GI/GII; p < 0.0001 in GII/GIII and p < 0.00001 in GI/GIII.

CONCLUSION

Light stimulates the cornea which is innervated by the trigeminal nerves. This experimental study indicates that the pupil remains mydriatic as the cornea is damaged by trigeminal ischemia following SAH and blocks the light flow.

The Role of Automated Infrared Pupillometry in Traumatic Brain Injury: A Narrative Review

Pupillometry, an integral component of neurological examination, serves to evaluate both pupil size and reactivity. The conventional manual assessment exhibits inherent limitations, thereby necessitating the development of portable automated infrared pupillometers (PAIPs). Leveraging infrared technology, these devices provide an objective assessment, proving valuable in the context of brain injury for the detection of neuro-worsening and the facilitation of patient monitoring. In cases of mild brain trauma particularly, traditional methods face constraints. Conversely, in severe brain trauma scenarios, PAIPs contribute to neuro-prognostication and non-invasive neuromonitoring. Parameters derived from PAIPs exhibit correlations with changes in intracranial pressure. It is important to acknowledge, however, that PAIPs cannot replace invasive intracranial pressure monitoring while their widespread adoption awaits robust support from clinical studies. Ongoing research endeavors delve into the role of PAIPs in managing critical neuro-worsening in brain trauma patients, underscoring the non-invasive monitoring advantages while emphasizing the imperative for further clinical validation. Future advancements in this domain encompass sophisticated pupillary assessment tools and the integration of smartphone applications, emblematic of a continually evolving landscape.

A smartphone pupillometry tool for detection of acute large vessel occlusion

OBJECTIVES

Pupillary light reflex (PLR) parameters can be used as quantitative biomarkers of neurological function. Since digital infrared pupillometry is expensive, we sought to examine alterations in PLR parameters using a smartphone quantitative pupillometry platform in subjects with acute ischemic stroke (AIS).

MATERIALS AND METHODS

Patients were enrolled if they presented to the emergency department as a stroke code activation and had evidence of a large vessel occlusion (LVO) on computed tomography angiography. Controls were enrolled from hospital staff. A smartphone pupillometer was used in AIS patients with LVO pre-mechanical thrombectomy, immediately post-thrombectomy, and at 24 h post-thrombectomy. Clinical and demographic data were collected, along with the proprietary Neurological Pupil index (NPi) score from the NPi-200 digital infrared pupillometer. PLR parameters were compared using mean differences. The absolute and non-absolute inter-eye difference in each parameter for each subject were also analyzed by measuring 1 - (R:L) to determine alteration in the equilibrium between subject pupils. The NPi was analyzed for mean differences between cohorts.

RESULTS

Healthy controls (n = 132) and AIS patients (n = 31) were enrolled. Significant differences were observed in PLR parameters for healthy subjects when compared to pre-thrombectomy subjects in both mean and absolute inter-eye differences after post hoc Bonferroni correction. The proprietary NPi score was not significantly different for all groups and comparisons.

CONCLUSIONS

Significant alterations in the PLR were observed in AIS patients with LVO before thrombectomy, indicating the potential use of smartphone pupillometry for detection of LVO.

Pupillary dynamics predict long-term outcome in a cohort of acute traumatic brain injury coma patients

OBJECTIVE

Examining the size and reactivity of the pupils of traumatic brain injury coma patients is fundamental in the Neuro-intensive care unit (ICU). Pupil parameters on admission predict long-term clinical outcomes. However, little is known about the dynamics of pupillary parameters and their potential value for outcome prediction.

METHODS

This study applied a time-course analysis of pupillary signals (size and photo-reactivity) in acute traumatic brain injury coma patients (n = 20) to predict outcome at 6 months.

RESULTS

The time course of pupillary signals was informative in discriminating favorable (F) versus unfavorable (U) outcomes, with the highest correlation within the 1st week notwithstanding pharmacological sedation. Patients with favorable outcome at 6 months showed more consistent in time isochoric and photo-reactive pupils. In contrast, patients with an unfavorable outcome showed more variable measures that tended to stabilize toward pathological values.

INTERPRETATION

Time-dependent tracking of pupils' size and reactivity is a promising application for ICU monitoring and long-term prognosis. These findings support the usefulness of automatic tools for the dynamic, quantitative, and objective measurements of pupils.

Neurological Pupillary Index (NPi) Measurement Using Pupillometry and Outcomes in Critically Ill Children

Aim/objective Neurological Pupil Index (NPi), measured by automated pupillometry (AP), allows the objective assessment of pupillary light reflex (PLR). NPi ranges from 0 (non-reactive) to 5 (normal). In this study, we aimed to compare neurologic and functional outcomes in children admitted for neurologic injury with normal (≥3) versus abnormal (<3) NPi measured during their pediatric intensive care unit (PICU) stay. Materials and methods We conducted a retrospective chart review of children between one month and 18 years admitted to our PICU with a diagnosis of neurologic injury between January 2019 and June 2022. We collected demographic, clinical, pupillometer, and outcome data, including mortality, Pediatric Cerebral Performance Category (PCPC), Pediatric Overall Performance Category (POPC), and Functional Status Score (FSS) at admission, at discharge, and at the three to six-month follow-up. We defined abnormal pupil response as any NPi <3 at any point during the PICU stay. Using the student's t-test and chi-square test, we compared the short-term and long-term outcomes of children with abnormal NPi (<3) versus those with normal NPi (≥3). Results There were 49 children who met the inclusion criteria and who had pupillometry data available for analysis. The mean (SD) Glasgow Coma Scale (GCS) in the study cohort was 5.6 (4.3), and 61% had low (<3) NPi during ICU stay. Mortality was significantly higher among patients with an abnormal NPi as compared to those with normal NPi. Children with abnormal NPi exhibited significant worsening of neurologic and functional status (ΔPCPC, ΔPOPC, and ΔFSS) from admission to discharge (mean (SD): 3.55(1.5), 3.45(1.43), 16.75(7.85), p<0.001) as compared to those with normal NPi (mean (SD): 1.45(0.93), 1.73(0.90), 3.55(2.07), p>0.05). The significant difference in neurologic and functional status persisted at the three to six-month follow-up between the two groups - children with abnormal NPi (mean (SD): 2.0(1.41), 2.08(1.38), 6.92(6.83), p<0.01) and children with normal NPi (mean (SD): 0.82(1.01), 0.94(1.03), 1.53(1.70), p>0.05). Conclusion In our retrospective cohort study, children admitted to the PICU for a neuro injury and with abnormal NPi (< 3) have higher mortality, and worse short-term and long-term neurologic and functional outcomes as compared to those with normal NPi (≥ 3) measured during the PICU course. AP provides an objective assessment of PLR and has potential applications for neuro-prognostication. More research needs to be done to elucidate the prognostic value of NPi in pediatrics.

Validation of a Smartphone Pupillometry Application in Diagnosing Severe Traumatic Brain Injury

The pupillary light reflex (PLR) is an important biomarker for the detection and management of traumatic brain injury (TBI). We investigated the performance of PupilScreen, a smartphone-based pupillometry app, in classifying healthy control subjects and subjects with severe TBI in comparison to the current gold standard NeurOptics pupillometer (NPi-200 model with proprietary Neurological Pupil Index [NPi] TBI severity score). A total of 230 PLR video recordings taken using both the PupilScreen smartphone pupillometer and NeurOptics handheld device (NPi-200) pupillometer were collected from 33 subjects with severe TBI (sTBI) and 132 subjects who were healthy without self-reported neurological disease. Severe TBI status was determined by Glasgow Coma Scale (GCS) at the time of recording. The proprietary NPi score was collected from the NPi-200 pupillometer for each subject. Seven PLR curve morphological parameters were collected from the PupilScreen app for each subject. A comparison via t-test and via binary classification algorithm performance using NPi scores from the NPi-200 and PLR parameter data from the PupilScreen app was completed. This was used to determine how the frequently used NPi-200 proprietary NPi TBI severity score compares to the PupilScreen app in ability to distinguish between healthy and sTBI subjects. Binary classification models for this task were trained for the diagnosis of healthy or severe TBI using logistic regression, k-nearest neighbors, support vector machine, and random forest machine learning classification models. Overall classification accuracy, sensitivity, specificity, area under the curve, and F1 score values were calculated. Median GCS was 15 for the healthy cohort and 6 (interquartile range 2) for the severe TBI cohort. Smartphone app PLR parameters as well as NPi from the digital infrared pupillometer were significantly different between healthy and severe TBI cohorts; 33% of the study cohort had dark eye colors defined as brown eyes of varying shades. Across all classification models, the top performing PLR parameter combination for classifying subjects as healthy or sTBI for PupilScreen was maximum diameter, constriction velocity, maximum constriction velocity, and dilation velocity with accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score of 87%, 85.9%, 88%, 0.869, and 0.85, respectively, in a random forest model. The proprietary NPi TBI severity score demonstrated greatest AUC value, F1 score, and sensitivity of 0.648, 0.567, and 50.9% respectively using a random forest classifier and greatest overall accuracy and specificity of 67.4% and 92.4% using a logistic regression model in the same classification task on the same dataset. The PupilScreen smartphone pupillometry app demonstrated binary healthy versus severe TBI classification ability greater than that of the NPi-200 proprietary NPi TBI severity score. These results may indicate the potential benefit of future study of this PupilScreen smartphone pupillometry application in comparison to the NPi-200 digital infrared pupillometer across the broader TBI spectrum, as well as in other neurological diseases.

Pupillometry in the follow-up of patients undergoing EVT - prediction of space-occupying hemispheric infarction

BACKGROUND

Despite benefits of endovascular treatment (EVT) for large vessel occlusion (LVO) ischemic stroke, space-occupying brain edema (BE) represents a detrimental complication. In critical-care settings, CT-imaging is needed for monitoring these patients. Yet, bed-side techniques with the potential to predict whether patients develop BE or not would facilitate a time- and cost-efficient patient care. We assessed clinical significance of automated pupillometry in the follow-up of patients undergoing EVT.

METHODS

From 10/2018 to 10/2021, neurocritical-care-unit patients were retrospectively enrolled after EVT of anterior circulation LVO. We monitored parameters of pupillary reactivity [light-reflex-latency (Lat), constriction- and redilation-velocities (CV, DV), percentage-change-of-apertures (per-change); NeurOptics-pupilometer®] up to every hour on day 1-3 of ICU stay. BE was defined as midline shift ≥ 5 mm on follow-up imaging 3-5 days after EVT. We calculated mean values of intra-individual differences between successive pairs of parameters (mean-deltas), determined best discriminative cut-off values for BE development (ROC-analyses), and evaluated prognostic performance of pupillometry for BE development (sensitivity/specificity/positive-/negative-predictive-values).

RESULTS

3241 pupillary assessments of 122 patients [67 women, 73 years (61.0-85.0)] were included. 13/122 patients developed BE. Patients with BE had significantly lower CVs, DVs, and smaller per-changes than patients without BE. On day 1 after EVT mean-deltas of CV, DV, and per-changes were significantly lower in patients with than without BE. Positive-predictive-values of calculated thresholds to discriminate both groups were considerably low, yet, we found high negative-predictive-values for CV, DV, per-changes, and mean-deltas (max.: 98.4%).

CONCLUSION

Our data suggest associations between noninvasively detected changes in pupillary reactivity and BE early after LVO-EVT. Pupillometry may identify patients who are unlikely to develop BE and may not need repetitive follow-up-imaging or rescue-therapy.

Neurological Pupil Index and Delayed Cerebral Ischemia after Subarachnoid Hemorrhage: A Retrospective Multicentric Study

BACKGROUND

Delayed cerebral ischemia (DCI) occurs in around 30% of patients suffering from nontraumatic subarachnoid hemorrhage (SAH) and is associated with poor neurological outcome. Whether the Neurological Pupil index (NPi) derived from the automated pupillometry could help to diagnose the occurrence of DCI remains unknown. The aim of this study was to investigate the association of NPi with the occurrence of DCI in patients with SAH.

METHODS

This was a multicenter, retrospective cohort study of consecutive patients with SAH admitted to the intensive care units of five hospitals between January 2018 and December 2020 who underwent daily NPi recordings (every 8 h) during the first 10 days of admission. DCI was diagnosed according to standard definitions (in awake patients) or based on neuroimaging and neuromonitoring (in sedated or unconscious patients). An NPi < 3 was defined as abnormal. The primary outcome of the study was to assess the time course of daily NPi between patients with DCI and patients without DCI. Secondary outcome included the number of patients who had an NPi < 3 before DCI.

RESULTS

A total of 210 patients were eligible for the final analysis; DCI occurred in 85 (41%) patients. Patients who developed DCI had similar values of mean and worst daily NPi over time when compared with patients without DCI. Patients with DCI had a higher proportion of at least one NPi < 3 at any moment before DCI when compared with others (39/85, 46% vs. 35/125, 38%, p = 0.009). Similarly, the worst NPi before DCI diagnosis was lower in the DCI group when compared with others (3.1 [2.5-3.8] vs. 3.7 [2.7-4.1], p = 0.05). In the multivariable logistic regression analysis, the presence of NPi < 3 was not independently associated with the development of DCI (odds ratio 1.52 [95% confidence interval 0.80-2.88]).

CONCLUSIONS

In this study, NPi measured three times a day and derived from the automated pupillometry had a limited value for the diagnosis of DCI in patients with SAH.

Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest

BACKGROUND

Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors.

METHODS

A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format.

RESULTS

Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors.

CONCLUSIONS

These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.

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.

Pupillary light reflex measured with quantitative pupillometry has low sensitivity and high specificity for predicting neuroworsening after traumatic brain injury

BACKGROUND

Triage and neurological assessment of the 1.7 million traumatic brain injuries occurring annually is often done by nurse practitioners and physician assistants in the emergency department. Subjective assessments, such as the neurological examination that includes evaluation of the pupillary light reflex (PLR), can contain bias. Quantitative pupillometry (QP) standardizes and objectifies the PLR examination. Additional data are needed to determine whether QP can predict neurological changes in a traumatic brain injury (TBI) patient.

PURPOSE

This study examines the effectiveness of QP in predicting neurological decline within 24 hours of admission following acute TBI.

METHODOLOGY

This prospective, observational, clinical trial used pragmatic sampling to assess PLR in TBI patients using QP within 24 hours of ED admission. Chi-square analysis was used to determine change in patient status, through Glasgow Coma Scale (GCS), at baseline and within 24 hours of admission, to the QP.

RESULTS

There were 95 participants included in the analysis; of whom 35 experienced neuroworsening, defined by change in GCS of >2 within the first 24 hours of admission. There was a significant association between an abnormal Neurological Pupil index (NPi), defined as NPi of <3, and neuroworsening (p < .0001). The sensitivity (51.43%) and specificity (91.67%) of abnormal NPi in predicting neuroworsening were varied.

CONCLUSION

There is a strong association between abnormal NPi and neuroworsening in the sample of TBI patients with high specificity and moderate sensitivity.

IMPLICATIONS

NPi may be an early indicator of neurological changes within 24 hours of ED admission in patients with TBI.

Can Quantitative Pupillometry be used to Screen for Elevated Intracranial Pressure? A Retrospective Cohort Study

BACKGROUND

Elevated intracranial pressure (ICP) is a serious complication in brain injury. Because of the risks involved, ICP is not monitored in all patients at risk. Noninvasive screening tools to identify patients with elevated ICP are needed. Anisocoria, abnormal pupillary size, and abnormal pupillary light reflex are signs of high ICP, but manual pupillometry is arbitrary and subject to interrater variability. We have evaluated quantitative pupillometry as a screening tool for elevated ICP.

METHODS

We performed a retrospective observational study of the association between Neurological Pupil index (NPi), measured with the Neuroptics NPi-200 pupillometer, and ICP in patients routinely monitored with invasive ICP measurement in the intensive care unit. We performed a nonparametric receiver operator curve analysis for ICP ≥ 20 mm Hg with NPi as a classification variable. We performed a Youden analysis for the optimal NPi cutoff value and recorded sensitivity and specificity for this cutoff value. We also performed a logistic regression with elevated ICP as the dependent variable and NPi as the independent variable.

RESULTS

We included 65 patients with invasive ICP monitoring. A total of 2,705 measurements were analyzed. Using NPi as a screening tool for elevated ICP yielded an area under receiver operator curve of 0.72. The optimal mean NPi cutoff value to rule out elevated ICP was ≥ 3.9. The probability of elevated ICP decreased with increasing NPi, with an odds ratio of 0.55 (0.50, 0.61).

CONCLUSIONS

Screening with NPi may inform high stakes clinical decisions by ruling out elevated ICP with a high degree of certainty. It may also aid in estimating probabilities of elevated ICP. This can help to weigh the risks of initiating invasive ICP monitoring against the risks of not doing so. Because of its ease of use and excellent interrater reliability, we suggest further studies of NPi as a screening tool for elevated ICP.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

Pupillometry in neurocritical care

Though pupillometer use is becoming more widespread, there is a lack of evidence on its use by nurses in the neurosurgical ICU. This article explores the use of the pupillometer among neurocritical care nurses to better understand their experience and the device's clinical utility.

The Function of the Autonomic Nervous System in Asian Patients With Chronic Migraine

Background

The pathogenic mechanisms underlying the autonomic nervous system (ANS) dysfunction in patients with chronic migraine (CM) remain unclear. This study investigated the pathogenesis of ANS dysfunction in this population.

Methods

A total of 60 patients diagnosed with CM and 60 healthy subjects were recruited to participate in this study. The pupil diameter, pupil contraction velocity, latency, amplitude, and the maximum gradient recovery time were examined before, at 2 min and at 5 min after the cold pressor test, which was combined with the pupillary light reflex method. A brain 3D T1-weighted structural imaging scan, resting-state functional magnetic resonance imaging scan, and diffusion tensor imaging (DTI) scan were also acquired.

Results

Patients with CM exhibited a longer recovery time to the maximum gradient at 2 min and at 5 min after cold pressing compared with the control group (P < 0.01 and P < 0.05, respectively). There was no significant difference in the pupil diameter, pupillary contraction velocity, latency, amplitude, blood pressure, or heart rate between the two groups (all P > 0.05). In the CM group, the regional homogeneity (ReHo) values of the left amygdala and left lateral hypothalamic area were significantly higher than those of other brain areas (P < 0.001, Alphasim corrected). The DTI scan of the whole brain area showed a lack of significant difference in DTI indices, including FA, MD, AD, and RD values between the two groups (P > 0.05, Alphasim corrected).

Conclusion

The dysfunction of the left amygdala and left lateral hypothalamic area may be related to ANS dysfunction in patients with CM.

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

Background

Electroencephalography (EEG) is widely used to monitor critically ill patients. However, EEG interpretation requires the presence of an experienced neurophysiologist and is time-consuming. Aim of this study was to evaluate whether parameters derived from an automated pupillometer (AP) might help to assess the degree of cerebral dysfunction in critically ill patients.

Methods

Prospective study conducted in the Department of Intensive Care of Erasme University Hospital in Brussels, Belgium. Pupillary assessments were performed using the AP in three subgroups of patients, concomitantly monitored with continuous EEG: "anoxic brain injury", "Non-anoxic brain injury" and "other diseases". An independent neurologist blinded to patient's history and AP results scored the degree of encephalopathy and reactivity on EEG using a standardized scale. The mean value of Neurologic Pupil Index (NPi), pupillary size, constriction rate, constriction and dilation velocity (CV and DV) and latency for both eyes, obtained using the NPi®-200 (Neuroptics, Laguna Hills, CA, USA), were reported.

Results

We included 214 patients (mean age 60 years, 55% male). EEG tracings were categorized as: mild (n = 111, 52%), moderate (n = 65, 30%) or severe (n = 16, 8%) encephalopathy; burst-suppression (n = 19, 9%) or suppression background (n = 3, 1%); a total of 38 (18%) EEG were classified as "unreactive". We found a significant difference in all pupillometry variables among different EEG categories. Moreover, an unreactive EEG was associated with lower NPi, pupil size, pupillary reactivity, CV and DV and a higher latency than reactive recordings. Low DV (Odds ratio 0.020 [95% confidence intervals 0.002-0.163]; p < 0.01) was independently associated with an unreactive EEG, together with the use of analgesic/sedative drugs and high lactate concentrations. In particular, DV values had an area under the curve (AUC) of 0.86 [0.79-0.92; p < 0.01] to predict the presence of unreactive EEG. In subgroups analyses, AUC of DV to predict unreactive EEG was lower (0.72 [0.56-0.87]; p < 0.01) in anoxic brain injury than Non-anoxic brain injury (0.92 [0.85-1.00]; p < 0.01) and other diseases (0.96 [0.90-1.00]; p < 0.01).

Conclusions

This study suggests that low DV measured by the AP might effectively identify an unreactive EEG background, in particular in critically ill patients without anoxic brain injury.

The Importance of Neuromonitoring in Non Brain Injured Patients

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901.

Use of Automated Infrared Pupillometry to Predict Delirium in the Intensive Care Unit: A Prospective Observational Study

Introduction

Delirium is an acute state of brain dysfunction prevalent among critically ill patients. Disturbances in the sympathetic neurons, including cholinergic neurons, have been reported to cause delirium by upsetting the balance of neurotransmitter synthesis, release, and inactivation. The cholinergic system mediates pupillary constriction as a response to light stimulation, and this reflex can be measured using automated infrared pupillometry (AIP). The relationship between delirium and AIP parameters has been examined. The Confusion Assessment Method for the Intensive Care Unit (CAM ICU) and the Intensive Care Unit Delirium Screening Checklist (ICDSC) are used for assessing delirium. However, that between the ICDSC score and AIP parameters remains unclear.

Objective

To examine the relationship between AIP parameters and the various categories of delirium as defined by the ICDSC score (delirium, subsyndromal delirium, no delirium).

Methods

This prospective observational study included patients aged ≥18 years admitted to the intensive care unit (ICU) from May 2018 to September 2018. ICU patients were classified into delirium, subsyndromal delirium, and no delirium groups according to the ICDSC score during ICU stay. The pupillary light reflex was assessed in both eyes immediately after admission using AIP with a portable infrared pupillometer. Logistic regression analyses were used to estimate the odds ratio to examine the relationship between the severity of delirium as assessed by the ICDSC score and the AIP parameters.

Results

In total 133 patients were included in the study. Based on the ICDSC scores, 41.4% of patients had no delirium, 40.6% had subsyndromal delirium, and 18% had delirium. Dilation velocity (DV) measured by AIP was significantly different among the delirium, subsyndromal delirium, and no delirium groups. Post-hoc comparisons showed that DV was significantly slower in the delirium group than in the no delirium group but was not significantly different between the subsyndromal delirium and no delirium groups. After adjusting for patients’ sex and age at enrollment, DV was shown to be independently associated with delirium.

Conclusion

This study suggests that the use of AIP at ICU admission may improve the identification of patients at a high risk of developing delirium.

Associations between the pupil light reflex and the broader autism phenotype in children and adults

The pupil light reflex (PLR), a marker of neuronal response to light, is a well-studied index of autonomic functioning. Studies have found that autistic children and adults have slower and weaker PLR responses compared to non-autistic peers, suggesting lower autonomic control. Altered autonomic control has also been associated with increased sensory difficulties in autistic children. With autistic traits varying in the general population, recent studies have begun to examine similar questions in non-autistic individuals. The current study looked at the PLR in relation to individual differences in autistic traits in non-autistic children and adults, asking how differences in the PLR could lead to variation in autistic traits, and how this might change across development. Children and adults completed a PLR task as a measure of sensitivity to light and autonomic response. Results showed that, in adults, increased levels of restricted and repetitive behaviors (RRB) were associated with a weaker and slower PLR. However, in children, PLR responses were not associated with autistic traits. Differences in PLR were also found across age groups, with adults showing smaller baseline pupil diameter and stronger PLR constriction as compared with children. The current study expanded on past work to examine the PLR and autistic traits in non-autistic children and adults, and the relevance of these findings to sensory processing difficulties is discussed. Future studies should continue to examine the neural pathways that might underlie the links between sensory processing and challenging behaviors.

Early Pupillometry Assessment in Traumatic Brain Injury Patients: A Retrospective Study

BACKGROUND

The aim of this study was to evaluate whether the early assessment of neurological pupil index (NPi) values derived from automated pupillometry could predict neurological outcome after traumatic brain injury (TBI).

METHODS

Retrospective observational study including adult (>18 years) TBI patients admitted from January 2018 to December 2020, with available NPi on admission. Abnormal NPi was considered if <3. Unfavorable neurological outcome (UO) at hospital discharge was considered for a Glasgow Outcome Scale of 1-3.

RESULTS

100 patients were included over the study period (median age 48 (34-69) years and median GCS on admission 11 (6-15)); 49 (49%) patients had UO. On admission, 20 (20%) patients had an abnormal NPi (NPi < 3); median worst (i.e., from both eyes) NPi was 4.2 (3.2-4.5). Median worst and mean NPi on admission were significantly lower in the UO group than others (3.9 (1.7-4.4) vs. 4.4 (3.7-4.6); p = 0.005-4.0 (2.6-4.5) vs. 4.5 (3.9-4.7); p = 0.002, respectively). The ROC curve for the worst and mean NPi showed a moderate accuracy to predict UO (AUC 0.66 (0.56-0.77); p = 0.005 and 0.68 (0.57-0.78); p = 0.002). However, in a generalized linear model, the prognostic role of NPi on admission was limited.

CONCLUSIONS

Low NPi on admission has limited prognostic value in TBI.

Detection of opioid effect with pupillometry

BACKGROUND

Opioids produce pupillary constriction but their impact on pupillary unrest and the dynamic parameters of the pupillary light reflex have not been characterized. Given the increasing use of portable pupillometers for care of critically ill patients, it is important to distinguish between opioid effects on the pupil versus those that have been reported to arise from traumatic and ischemic brain insults. We undertook this study to determine which pupillary responses are most profoundly and consistently affected by a progressive infusion of remifentanil.

METHODS

We studied the effect of remifentanil on the pupil using two portable infrared pupillometers in 18 volunteers. One pupillometer measured pupillary unrest in ambient light (PUAL) and the other pupillometer measured neurological pupillary index (NPi), constriction velocity (CV), pupil diameter (PD), latency, and % reflex (% reflex) following a transient light flash. Remifentanil was administered at predetermined weight-adjusted rates to raise opioid effect site concentration up to a range known to produce respiratory depression and oxyhemoglobin desaturation, based on a previously published pharmacokinetic model.

RESULTS

PUAL was ablated by remifentanil, declining 94 ± 6% from baseline at the time of maximum drug effect. Other pupillary measurements decreased 50-65% from baseline. NPi was unchanged. At the time of oxyhemoglobin desaturation, deviations in PD, CV, and % reflex were widely scattered, whereas PUAL consistently approached zero.

CONCLUSION

PUAL is a highly specific indicator of central opioid effect. As a non-invasive measure, it may provide useful data to clinicians who prescribe opioids.

Development and Implementation of a Handheld Pupillometer for Detection of Optic Neuropathies

Purpose: Quantitative pupillometry has utility in research settings for measuring optic nerve and autonomic function. We configured a portable device to perform quantitative pupillometry with application to detecting unilateral optic neuropathies in the clinical setting.Materials & methods: Light stimuli were delivered, and pupil diameter responses recorded using customized software implemented on a commercial portable electroretinography device. Increasing pupillary constriction occurred with increasing duration and intensity of full field blue light (470 nm) stimuli in healthy subjects. Flashes of 1 s dim (50 cd/m2) and bright (316 cd/m2) blue light were administered to both eyes of subjects with unilateral optic neuropathies (n = 10) and controls (n = 5). Maximum pupillary constriction (Cmax) for each stimulus was compared between control eyes and optic neuropathy eyes. Cmax for the inter-eye difference curve (Cdiffmax) was compared between control and optic neuropathy subjects.Results: The pupil protocol lasted 15 minutes and was well tolerated by subjects. Cmax for bright and dim stimuli was reduced in eyes with optic neuropathy compared to fellow and control eyes (p < .0005 for all). Inter-eye Cdiffmax was larger in optic neuropathy subjects than control subjects for both dim and bright stimuli (p = .002, <0.0005). There was no overlap between groups for Cmax and Cdiffmax for either stimulus.Conclusions: A portable pupillometer was implemented on a commercial portable electroretinography platform and applied in a pilot manner to subjects with and without unilateral optic neuropathies. Optic neuropathy eyes were distinguished from non-optic neuropathy eyes both within and between subjects.

The Expanding Role of Quantitative Pupillometry in the Evaluation and Management of Traumatic Brain Injury

Traumatic brain injury is a rapidly increasing source of morbidity and mortality across the world. As such, the evaluation and management of traumatic brain injuries ranging from mild to severe are under active investigation. Over the last two decades, quantitative pupillometry has been increasingly found to be useful in both the immediate evaluation and ongoing management of traumatic brain injured patients. Given these findings and the portability and ease of use of modern pupillometers, further adoption and deployment of quantitative pupillometers into the preclinical and hospital settings of both resource rich and medically austere environments.

Automated pupillometry helps monitor the efficacy of cardiopulmonary resuscitation and predict return of spontaneous circulation

BACKGROUND

We investigated the effectiveness of automated pupillometry on monitoring cardiopulmonary resuscitation (CPR) and predicting return of spontaneous circulation (ROSC) in a swine model of cardiac arrest (CA).

METHODS

Sixteen male domestic pigs were included. Traditional indices including coronary perfusion pressure (CPP), end-tidal carbon dioxide (ETCO₂), regional cerebral tissue oxygen saturation (rSO₂) and carotid blood flow (CBF) were continuously monitored throughout the experiment. In addition, the pupillary parameters including the initial pupil size before constriction (Init, maximum diameter), the end pupil size at peak constriction (End, minimum diameter), and percentage of change (%PLR) were measured by an automated quantitative pupillometer at baseline, at 1, 4, 7 min during CA, and at 1, 4, 7 min during CPR.

RESULTS

ROSC was achieved in 11/16 animals. The levels of CPP, ETCO₂, rSO₂ and CBF were significantly greater during CPR in resuscitated animals than those non-resuscitated ones. Init and End were decreased and %PLR was increased during CPR in resuscitated animals when compared with those non-resuscitated ones. There were moderate to good significant correlations between traditional indices and Init, End, and %PLR (|r| = 0.46-0.78, all P < 0.001). Furthermore, comparable performance was also achieved by automated pupillometry (AUCs of Init, End and %PLR were 0.821, 0.873 and 0.821, respectively, all P < 0.05) compared with the traditional indices (AUCs = 0.809-0.946).

CONCLUSION

The automated pupillometry may serve as an effective surrogate method to monitor cardiopulmonary resuscitation efficacy and predict ROSC in a swine model of cardiac arrest.

Automated Quantitative Pupillometry in the Critically Ill: A Systematic Review of the Literature

OBJECTIVE

A systematic literature search has been performed to identify potential confounders for outcome prediction using pupillary light reflex in adult critically ill patients, as measured by handheld automated pupillometry devices.

METHODS

Three digital databases (PubMed, EmBase, Cochrane) were systematically searched. Articles published between 1990 and 2019 in adult patients using monocular automated handheld devices were considered. Studies were classified according to the Oxford Centre for Evidence-Based Medicine classification (level 1 represents the highest and level 5 the lowest level of evidence). Case reports, original research, and systematic reviews were included and cross-referenced.

RESULTS

With the use of 202 search terms, 58 eligible articles reporting the use of handheld pupillometry in the critically ill could be identified, considering 3,246 patients. The highest level of evidence came from 10 randomized trials and 19 prospective observational studies. The level of evidence was mostly 2 to 3 and highest with studies regarding the potential confounding effects of pain, the use of opioids, and increased intracranial pressure. Additional potential confounders found are selective serotonin reuptake inhibitors, α2-adregenic receptor agonists, and NMDA antagonists.

CONCLUSIONS

The pupillary light reflex is susceptible to factors resulting from underlying comorbid conditions and effects of treatment regimens. Scenarios frequently encountered in critical care such as pain, use of opioids, and proof of increased intracranial pressure have potential confounding effects on outcome and pupillary reflexes. When treatment is guided by pupillary metrics, such confounders put patients at risk of overtreatment or undertreatment. Future research should validate and identify additional confounders, because our review suggests that even more unexplored confounders may exist.

Abnormal neurological pupil index is associated with malignant cerebral edema after mechanical thrombectomy in large vessel occlusion patients

BACKGROUND AND OBJECTIVES

Malignant cerebral edema (MCE) is a feared complication in patients suffering from large vessel occlusion. Variables associated with the development of MCE have not been clearly elucidated. Use of pupillometry and the neurological pupil index (NPi) as an objective measure in patients undergoing mechanical thrombectomy (MT) has not been explored. We aim to evaluate variables significantly associated with MCE in patients that undergo MT and hypothesize that abnormal NPi is associated with MCE in this population.

METHODS

A retrospective analysis of patients with acute ischemic stroke who had undergone MT at our institution between 2017 and 2020 was performed. Baseline and outcome variables were collected, including NPi values from pupillometry readings of patients within 72 h after the MT. Patients were divided into two groups: MCE versus non-MCE group. A univariate and multivariate analysis was performed.

RESULTS

Of 284 acute ischemic stroke patients, 64 (22.5%) developed MCE. Mean admission glucose (137 vs. 173; p < 0.0001), NIHSS on admission (17 vs. 24; p < 0.01), infarct core volume (27.9 vs. 17.9 mL; p = 0.0036), TICI score (p = 0.001), and number of passes (2.9 vs. 1.8; p < 0.0001) were significantly different between the groups. Pupillometry data was present for 64 patients (22.5%). Upon multivariate analysis, abnormal ipsilateral NPi (OR 21.80 95% CI 3.32-286.4; p = 0.007) and hemorrhagic conversion were independently associated with MCE.

CONCLUSION

Abnormal NPi and hemorrhagic conversion are significantly associated with MCE in patients following MT. Further investigation is warranted to better define an association between NPi and patient outcomes in this patient population.

Feasibility study of a smartphone pupillometer and evaluation of its accuracy

Measurement of pupillary characteristics, such as pupillary unrest in ambient light, and reflex dilation have been shown to be useful in a variety of clinical situations. Dedicated pupillometers typically capture images in the near-infrared to allow imaging in both light and darkness. However, because a subset of pupillary measurements can be acquired with levels of visible light suitable for conventional cameras, it is theoretically possible to capture data using general purpose cameras and computing devices such as those found on smartphones. Here we describe the development of a smartphone-based pupillometer and compare its performance with a commercial pupillometer. Smartphone pupillometry software was developed and then compared with a commercial pupillometer by performing simultaneous scans in both eyes, using the smartphone pupillometer and a commercial pupillometer. The raw scans were compared, as well as a selected pupillary index: pupillary unrest in ambient light. In 77% of the scans the software was able to successfully identify the pupil and iris. The raw data as well as calculated values of pupillary unrest in ambient light were in clinically acceptable levels of agreement; Bland-Altman analysis of raw pupil measurements yielded a 95% confidence interval of 0.26 mm. In certain situations a smartphone pupillometer may be an appropriate alternative to a commercial pupillometer.