OBJECTIVES There is growing interest in the ability of automated infrared pupillometry to assess severity of neurological illness. We studied the correlation between computed tomography (CT) indicators of intracerebral hemorrhage (ICH) severity with objective measures of the pupillary light reflex (PLR), and hypothesized that hemorrhage volume would predict the Neurological Pupil index™ (NPi™), an indicator of pupillary reactivity. METHODS This study examined data from patients with supratentorial ICH who underwent serial pupillometer evaluations. CT images were examined to determine the location and laterality of the hemorrhage, along with hematoma volume (using the simplified ABC/2 method), midline shift, hydrocephalus score, and modified Graeb score (indicating interventricular hemorrhage). Demographics were examined with standard measures of central tendency, hypotheses with logistic regression, categorical data with Fisher's Exact X2, and multivariate modeling with constructed MAX-R models. RESULTS Data were gathered from 44 subjects. ICH volume exhibited the strongest correlation with NPi (ipsilateral [r2 = 0.48, p < 0.0001, contralateral [(r2 = 0.39, p < 0.0001]). Horizontal midline shift of the septum pellucidum also correlated with NPi (ipsilateral [r2 = 0.25, p = 0.0006], contralateral [r2 = 0.15, p = 0.0106]), as did shift of the pineal gland (ipsilateral [r2 = 0.21, p = 0.0017], contralateral[r2 = 0.11, p = 0.0328]). ICH volume was the most predictive of abnormal NPi (AUC = 0.85 for ipsilateral and 0.88 for contralateral NPi), and multivariate modeling identified additional independent predictors of NPi. CONCLUSION ICH volume and shift of midline structures correlate with NPi, and abnormalities in NPi can be predicted by hematoma volume and other CT indicators of ICH severity. Future studies should explore the role of NPi in detecting early hematoma expansion and worsening midline shift.

中文翻译：

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幕上脑出血量和其他 CT 变量预测神经瞳孔指数

目标人们对自动红外瞳孔测量法评估神经系统疾病严重程度的能力越来越感兴趣。我们研究了脑出血 (ICH) 严重程度的计算机断层扫描 (CT) 指标与瞳孔光反射 (PLR) 的客观测量之间的相关性，并假设出血量可以预测神经学瞳孔指数™ (NPi™)，这是一个指标瞳孔反应。方法 本研究检查了接受连续瞳孔计评估的幕上 ICH 患者的数据。检查 CT 图像以确定出血的位置和侧向性，以及血肿体积（使用简化的 ABC/2 方法）、中线偏移、脑积水评分和改良的 Graeb 评分（指示脑室出血）。使用集中趋势的标准测量、逻辑回归假设、Fisher's Exact X2 的分类数据以及构建的 MAX-R 模型的多变量建模来检查人口统计学。结果 数据来自 44 名受试者。ICH 体积与 NPi 的相关性最强（同侧 [r2 = 0.48，p < 0.0001，对侧 [（r2 = 0.39，p < 0.0001]）。透明隔的水平中线移位也与 NPi（同侧 [r2 = 0.25， p = 0.0006]，对侧 [r2 = 0.15，p = 0.0106]），以及松果体移位（同侧 [r2 = 0.21，p = 0.0017]，对侧 [r2 = 0.11，p = 0.0328]）ICH 体积。是 NPi 异常的最预测因素（同侧 AUC = 0.85，对侧 NPi 为 0.88），多变量模型确定了 NPi 的其他独立预测因子。结论 ICH 体积和中线结构的偏移与 NPi 相关，NPi 的异常可以通过血肿体积和其他 ICH 严重程度的 CT 指标来预测。未来的研究应探讨 NPi 在检测早期血肿扩大和中线移位恶化中的作用。