Quantitative assessments of pupillary light reflexes in neurocritically ill patients

Kim, Tae Jung

doi:10.18700/jnc.220085

Cited by 7 publications

(8 citation statements)

References 47 publications

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“…In particular, quantitative assessment of the PLR using an automated pupillometer is a reliable and objective method for monitoring pupillary reactivity. Pupillometer assessment parameters, such as the NPi and CV, are reportedly sensitive to changes in neurological deterioration, which allows rapid diagnosis and evaluation of intracranial pathology and outcomes that assist in clinical decision-making following acute brain injuries [1][2][3][4]6,7,[14][15][16]. Pupillometer parameters on the ipsilateral and contralateral sides were similar.…”

Section: Discussionmentioning

confidence: 87%

“…When the neurological symptoms were stable, quantitative pupil assessment was performed using NPi-100 Pupillometer (Neu-rOptics Inc.) every 4 hours by neurointensivists or neuroICU nurses in charge of the standard care of patients. The pupillometer parameters of maximum pupil size, percentage of constriction (%CH), constriction velocity (CV), maximum constriction velocity (MCV), dilation velocity (DV), latency (LAT), and NPi in the right and left eyes were recorded at 0:00, 4:00, 8:00, 12:00, 16:00, and 20:00 [1,2,4]. The values recorded at 20:00 were considered as the baseline, as the lights were turned off at 00:30 in the neuroICU.…”

Section: Measurement Of Quantitative Pupillary Reactivitymentioning

confidence: 99%

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Diurnal variation in quantitative pupillary reactivity in large hemispheric stroke

Kim,

Lee,

Park

et al. 2023

J Neurocrit Care

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Background: Pupillary light reflex (PLR) assessment is an important neurological examination reflecting neurological deterioration in severe stroke cases. This study investigated the impact of diurnal variation in the PLR using quantitative pupillometry in stable patients with large hemispheric stroke.Methods: We included 35 patients with large hemispheric stroke without neurological worsening, who were admitted to the neurological intensive care unit between April 2017 and November 2021. Quantitative pupillometry was performed every 4 hours. Pupillometer parameters of maximum pupil size, percentage of constriction (%CH), latency (LAT), constriction velocity (CV), dilation velocity (DV), maximum constriction velocity (MCV), and neurological pupil index (NPi) score were recorded. We evaluated changes in the pupillometer parameters over time using linear mixed model analysis.Results: The diurnal variations revealed that the following parameters were significantly higher at 04:00 than at 20:00: maximum pupil size (right [Rt]: 3.59 vs. 3.21 mm, P<0.001; left [Lt]: 3.51 vs. 3.18 mm, P<0.001), %CH (Rt: 31.48 vs. 25.72, P<0.001; Lt: 31.42 vs. 24.98, P<0.001), CV (Rt: 1.97 vs. 1.68 mm/sec, P<0.001; Lt: 1.98 vs. 1.65 mm/sec, P<0.001), and DV (Rt: 0.97 vs. 0.84 mm/sec, P<0.001; Lt: 0.94 vs. 0.82 mm/sec, P=0.001). However, no significant diurnal variations were observed in the NPi values. Conclusion: Pupillary dynamics based on quantitative pupillometer parameters, including the NPi, demonstrated diurnal variations over 24 hours in large hemispheric stroke patients without neurological worsening. However, all changes in the pupillometer parameters were within normal ranges.

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Section: Discussionmentioning

confidence: 87%

Section: Measurement Of Quantitative Pupillary Reactivitymentioning

confidence: 99%

Diurnal variation in quantitative pupillary reactivity in large hemispheric stroke

Kim,

Lee,

Park

et al. 2023

J Neurocrit Care

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“…Quantitative pupillometry plays an increasingly important in critically ill patients, both for the evaluation of neurological status ( 7 , 12 , 13 ), monitoring ( 12 , 13 ), and prognostication ( 12 , 13 , 17 ). These especially include patients affected by acute brain injuries ( 14 , 17 ), where there is a need to detect deterioration of neurological state.…”

Section: Discussionmentioning

confidence: 99%

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

Bogucki,

John,

Zinkiewicz

et al. 2024

Front. Neurol.

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IntroductionThe 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 resultsWe 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.DiscussionThis 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.

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“…Quantitative pupillometry plays an increasingly important in critically ill patients, both for the evaluation of neurological status ([14, 16, 28]), monitoring ([16, 28]), and prognostication ([16, 26, 28]). These especially include patients affected by acute brain injuries ([4, 26]), where there is a need to detect deterioration of neurological state.…”

Section: Discussionmentioning

confidence: 99%

“…The PLR is an important marker of neurological decline in patients with conditions such as traumatic brain injury, stroke, infection, tumors, and aneurysms due to raised intracranial pressure (ICP) ([4–7, 9, 16, 18, 21, 25, 26, 28, 29, 34] Furthermore, the degree of PLR reactivity has recently been shown to hold prognostic value in patients with intracranial haemorrhage and traumatic brain injury ([4, 7, 21, 25, 26, 29]). It is therefore crucial for measured pupil parameters to be accurate.…”

Section: Introductionmentioning

confidence: 99%

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

Bogucki,

John,

Zinkiewicz

et al. 2023

Preprint

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The pupillary light reflex (PLR) involves the retina, brainstem, sympathetic and parasympathetic systems. It is therefore sensitive to a wide array of factors, including not only the neurological state of the patient but also the environment in which it is measured. The PLR is stimulated by using a single light pulse, which yields a complex pupil constriction waveform, that can be characterized by several parameters. To detect a pathological change in the PLR, it is therefore necessary to separate the contributions of neuro-ophthalmic factors from environmental variables. The strongest environmental factor affecting PLR is ambient illumination, which can vary over several orders of magnitude and is difficult to control due to diurnal, seasonal, and location variations. We assessed the sensitivity of seven PLR parameters using a smartphone-based pupillometer (AI Pupillometer, Solvemed Inc.) to differences in ambient light intensity. Ten subjects underwent 388 measurements in ambient conditions ranging from complete darkness (<5 lx) to bright lighting (≲10000 lx). Lighting most strongly affected the initial pupil size, constriction amplitude, and velocity. Nonlinear models were fitted to find the correction factor that maximally stabilized PLR parameters across different ambient light levels. To demonstrate that the correction factor improved the detection of an unreactive pupil, 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 used as predictors in a machine learning model to produce a scalar value, the neuro-pupillary index (NPx), an index that quantifies pupil reactivity. The index discriminated unreactive pupils with 100% accuracy and was stable under changes in ambient illumination across four orders of magnitude. The correction method proposed here mitigates 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.

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Quantitative assessments of pupillary light reflexes in neurocritically ill patients

Cited by 7 publications

References 47 publications

Diurnal variation in quantitative pupillary reactivity in large hemispheric stroke

Diurnal variation in quantitative pupillary reactivity in large hemispheric stroke

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

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

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