Background: Portable automated infrared pupillometry is becoming increasingly popular. To generate an objective reference base, the Neurological Pupil index (NPi) which combines different values of the pupillary light reflex is being introduced into clinical practice. In this explorative study, we examined different aspects of the NPi in relation to clinical severity and outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). Materials and Methods: Patients with serial assessment of the NPi (NeurOptics pupillometer NPi-200, Irvine, CA) starting no later than day 2 after aSAH onset were included in the study. Relative numbers of pathologic NPi's, absolute NPi values, and their variances were compared according to aSAH clinical severity grade, functional outcome, and case fatality. The correlation between NPi and intracranial pressure, and NPi periodicity, were also examined. Results: In total, 18 patients with 4456 NPi values were eligible for inclusion in the analysis. The general trend of the NPi over time reflected the course of the neurological illness. Mean NPi tended to be lower in patients with clinically severe compared with nonsevere aSAH (3.75±0.40 vs. 4.56±0.06; P=0.171), and in patients with unfavorable compared with favorable outcomes (3.64±0.48 vs. 4.50±0.08; P=0.198). The mean variance of the NPi was higher in patients with severe compared with nonsevere aSAH (0.49±0.17 vs. 0.06±0.02; P=0.025). Pathologic NPi values were recorded more frequently in patients with severe compared with nonsevere aSAH (16.3%±8.8% vs. 0.0%±0.0%; P=0.002), and in those with unfavorable compared with favorable outcomes (19.2%±10.6% vs. 0.7%±0.6%; P=0.017). NPi was inversely correlated with intracranial pressure (Spearman r=−0.551, P<0.001). We observed a circadian pattern of NPi's which was seemingly disrupted in patients with fatal outcome. Conclusions: On the basis of this preliminary study, the assessment of NPi by pupillometry is feasible and might complement multimodal neuromonitoring in patients with aSAH.

BACKGROUND: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. METHODS: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. RESULTS: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. CONCLUSIONS: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

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BACKGROUND: Intracranial pressure (ICP) and arterial blood pressure (ABP) are related to each other through cerebral autoregulation. Central venous pressure (CVP) is often measured to estimate cardiac filling pressures as an approximate measure for the volume status of a patient. Prior modelling efforts have formalized the functional relationship between CVP, ICP and ABP. However, these models were used to explain short segments of data during controlled experiments and have not yet been used to explain the slowly evolving ICP increase that occurs typically in patients after aneurysmal subarachnoid hemorrhage (SAH). OBJECTIVE: To analyze the functional relationship between ICP, ABP and CVP recorded from SAH patients in the first five days after aneurysm. METHODS: Two methods were used to elucidate this relationship on the running average of the signals: First, using Spearman correlation coefficients calculated over 30 min segments Second, for each patient, linear state space models of ICP as the output and ABP and CVP as inputs were estimated. RESULTS: The mean and variance of the data and the correlation coefficients between ICP-ABP and ICP-CVP vary over time as the patient progresses through their stay in the ICU. On average, after an SAH event, the models show that a) ABP is the bigger driver of changes in ICP than CVP and that increasing ABP leads to reduction in ICP and (b) increasing CVP leads to an increase in ICP. CONCLUSIONS: Finding a) agrees with the hypothesis that patients with subarachnoid hemorrhage have defective autoregulation, and b) agrees with the positive correlation observed between central venous pressure and intracranial pressure in the literature.