Bedside nursing assessment of the pupil is a key component of the neurological examination in neurocritical care patients.1 The pupillary examination has traditionally been used to monitor these patients; this examination includes assessment of pupil size, shape, symmetry, and reactivity to light.1-3 The pupillary light reflex (PLR), or reactivity to light, portion of the pupillary assessment has potential for early detection of expanding or emerging intracranial lesions.4 Current practice includes mostly subjective estimation of pupil size in millimeters, and it is common to hear descriptive terms such as "small," "moderate," or "large" accompanied by terms such as "brisk," "sluggish," or "fixed" to determine the reactivity of the pupil.1,5 Today, an objective way to perform pupillary examination is by using the automated pupillometer to obtain more reliable results6,7 and provide several new variables that are not available with subjective assessment (eg, constriction and dilation velocities).8

Recent studies have shown some association between pupillometer readings and intracranial pressure (ICP),2 neurological deterioration,9 pain,10,11 sedative response,12 and as a predictor of poor clinical outcomes.13 Many of these studies have been conducted with modern pupil technology, which allows the clinician to obtain objective or numerical values that are associated with the pupillary examination. The pupillometer used for this registry was the NPi-200 (NeurOptics, Inc). This device is the only handheld optical scanner available in the U.S.A. that objectively measures pupil size, and pupil reactivity in patients requiring neurological pupil examinations.8 There are no studies that explore associations between the pupillometer values and patient outcomes for patients with subarachnoid hemorrhage (SAH).14-16 This study will explore how PLR readings (size, CV, DV, NPi, and LAT) might predict clinical outcomes in patients with SAH. We hypothesize that the variation of pupillometer values is associated with modified Rankin Scale (mRS) at discharge.

Methods

This is a secondary analysis of a prospective multicenter END-PANIC registry.17 The pupillometer used for this registry was the NPi-200 (NeurOptics, Inc). This device is the only handheld optical scanner available in the U.S.A. that objectively measures pupil size and pupil reactivity in patients requiring neurological pupil examinations. These variables include maximal and minimal diameter, latency (LAT), constriction velocity (CV), dilation velocity (DV), and the Neurologic Pupil Index (NPi) (a proprietary algorithm unique to NeurOptics which computes an overall score of pupil reaction to light). The NPi is a unique new variable.8 Data were collected from 3 neurocritical intensive care units (NICUs) in the United States that use pupillometers as standard of care. Patients are included in the registry if they are older than 18 years and were admitted to the NICU with SAH (including aneurysmal SAH [aSAH]). The END-PANIC registry received approval by the institutional review board and is registered with http://ClinicalTrials.gov (NCT02804438).

The patient baseline data included age, sex, race, primary diagnosis (SAH and aSAH), severity (Hunt & Hess score), and admission Glasgow Coma Scale (GCS) and mRS. Clinical variables included pupillometer readings obtained during the NICU stay. The NICU and hospital length of stay (LOS), and mRS at discharge were recorded. Descriptive statistics included measures of central tendency (means, standard deviations, medians, and ranges) for continuous variables and frequencies and percentages for categorical variables.

The within-subject standard deviation (W_SD) values of PLR readings (size, CV, DV, NPi, and LAT) were first derived to provide an estimate of the spread of data for each individual patient. The W_SD values were then used as predictors of the mRS at discharge. Correlation and logistic regression analysis for normally distributed data was performed using SAS v9.4.

Results

Eighty-two patients were included in the data analysis, with a total of 4403 pupillary readings (range of readings, 2-384 per patient). Patient mean age was 57.7 years; 66% were female (n = 54); primary diagnosis was 77% SAH (n = 63) and 23% aSAH (n = 19); and Hunt & Hess scale showed 40% grade 1 (n = 33), 27% grade 2 (n = 22), and 33% grade 3 (n = 27), with no cases in Hunt & Hess grades 4 and 5. The admission GCS median score was 14 (eye, 4; verbal, 4; motor, 6). On admission, mRS distribution was as follows: 78.1% score 0 (n = 64), 8.5% score 1 (n = 7), 1.2% score 2 (n = 1), 4.9% score 3 (n = 4), 2.4% score 4 (n = 2), and 4.9% score 5 (n = 4). The discharge mRS distribution was as follows: 22% score 0 (n = 18), 13.4% score 1 (n = 11), 7.3% score 2 (n = 6), 6% score 3 (n = 5), 26.9% score 4 (n = 22), 13.4% score 5 (n = 11), and 11% score 6 (n = 9). The mean NICU LOS was 14.2 days with a range of 0 to 40 days, and that for hospital LOS was 17.7 days with a range of 0 to 56 days (Table 1). A Wilcoxon test indicates a statistical significance difference among the mRS on admission and at discharge (Sign test = 28, P < .0001).

TABLE 1 Baseline Characteristics of Demographic and Standard Deviation of Pupillometer Readings

To explore within-subject variation, the standard deviation for PLR values was computed for each patient. Bivariate analysis showed a moderate negative correlation with the dependent variable, mRS at discharge, and the W_SD for PLR values in both eyes (r = -0.3 to -0.47, P < .01; Table 2). A logistic regression after controlling for admission GCS indicated that W_SD for NPi, size, CV, and DV for both eyes are predictors of mRS at discharge ([beta] = -1.21 to -10.21, P < .05), suggesting that higher variation in pupillometer readings is associated with lower scores in mRS at discharge suggesting that higher variation in pupillometer readings is associated with lower scores in mRS at discharge (Supplemental Table 3, Supplemental Digital Content 1, available at http://links.lww.com/JNN/A167).

TABLE 2 Correlation for W

Discussion

Assessing the PLR provides vital information on intracranial dynamics.5,18 Findings from this study support the statement that the variation of pupillometer values may predict outcome measures at discharge. This suggests that patients whose visual pathways have more adaptation to change and response to treatment, showed in this study as higher W_SD PLR values, will be more likely to have better clinical outcomes (low scores in mRS at discharge) as evidenced by the negative correlation between W_SD PLR values and mRS at discharge.

The association between PLR and mRS may reflect dynamic changes in ICP unresponsive to therapy. As ICP increases, there is an increased risk for central brain herniation. The mechanical forces and brain compression potentially prevent transmission of electrical impulses along the oculomotor cranial nerve, resulting in delayed or absent constriction of the pupil.19 Although loss of PLR (fixed pupil) can and does occur without accompanying central herniation,20 a growing body of research has found associations between PLR assessed by automated pupillometry and changes in ICP.4 McNett et al2 published findings that show a statistically significant relationship between NPi less than 3 and increased ICPs. Zafar and Suarez21 suggest that PLR values are important in determining neurologic outcomes and prognosis for NICU patients, and Aoun et al22 suggest that NPi changes are strongly associated with the advent of delayed cerebral ischemia. The clinical relevance of these results strengthens the contention that PLR values could be an early herald of clinical changes.

The principal limitation of this study is that, despite the GCS being controlled for in the regression model, there may be other potential confounders that should be analyzed in future studies (eg, the Hunt & Hess score, nonreactive pupils that will have lower standard deviation and discriminate between diagnoses SAH/aSAH), and this might have influenced these results. Another recognized limitation is temporal variance. Nursing staff were tasked to perform serial PLR assessments, but the interval between assessments and the number of assessments per subject varied. Automated pupillometry is becoming more routine, and various devices are being marketed and found reliable.7,23 As the evolution in technology progresses, additional and more frequent data may reveal specific trends and relationships in PLR findings that alter the conclusions reached from this analysis.

Conclusion

Patients with higher W_SD in PLR values had lower (better) discharge mRS, suggesting that patients unable to respond to changes in intracranial dynamics are at a higher risk for poor outcomes. Pupillary light reflex values may be a marker for neurocritical care patients' outcomes.

References