Monitoring the depth of palliative sedation by video-pupillometry: A case report

Background: Palliative sedation is sometimes interrupted by undesired arousals. Pupillometry has been used in anesthesiology to monitor pain and sedation but has never been used during palliative sedation. Actual case: A 48 years-old patient, with multi-metastatic cancer, underwent palliative sedation to manage global suffering. On the second day, the patient experienced arousal which required medication adjustments to ensure pain relief and increased sedation. Possible course of action: Depth of sedation is monitored with clinical scales, such as the Richmond Agitation-Sedation Scale. But these scales do not measure brain stem activity and are poor at predicting arousal. Formulation of a plan: During palliative sedation, an infrared pupillometer was used to monitor pupil size and pupillary reactivity (Neurolight®, IDMed®, Marseille, France). Outcome: The pupillary light reflex was depressed during deep sedation. In our case, we observed a low-normal reflex along with dilated pupil before arousal. Lessons from the case: Our case suggests that reflex intensity and pupil size might predict arousals during palliative sedation. View on research problems, objectives, or questions generated by the case: Prospective studies are needed to confirm our findings. Pupillometry’s acceptability should also be questioned from patient’s, families’, and caregivers’ perspectives.

Does caffeine matter for arousal? Affective and autonomic responses induced by caffeine in coffee intake: evidence from a double-blind tasting task

Coffee is consumed worldwide, but there are different types of espresso blends, each with its unique concentration of caffeine, which can have different effects on the human being. The aim of this study was to understand the effect of the impact of caffeine on the autonomic nervous system, evaluating the physiological changes and subjective responses due to different levels of caffeine intake. A double-blind tasting task consisting of one within-subject factor design (caffeine level: high / double caffeine mixture (blend A) vs single-charge caffeine mixture (blend B) vs low-caffeine mixture (blend c) allowed us to assess participants’ autonomic responses using Heart Rate Variability (HRV) and Pupillary Reactivity (PR). Arousal was also assessed through the Self-Assessment Manikin (SAM). Results revealed statistically significant differences in HRV and PR between coffee blends, showing the blend A,a more pronounced autonomic response that blend C. However, no significant differences were found in arousal level among coffee blends. These results are similar to previous research that pointed out to a discordance between subjective and objective measures when caffeine is consumed.

Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

Automated Pupillometry Identifies Absence of Intracranial Pressure Elevation in Intracerebral Hemorrhage Patients

Introduction Although automated pupillometry is increasingly used in critical care settings, predictive value of automatically assessed pupillary parameters during different intracranial pressure (ICP) levels and possible clinical implications are unestablished. Methods This retrospective cohort study at the neurocritical care unit of the University of Erlangen-Nuremberg (2016–2018) included 23 nontraumatic supratentorial (intracerebral hemorrhage) ICH patients without signs of abnormal pupillary function by manual assessment, i.e., absent light reflex. We assessed ICP levels by an external ventricular drain simultaneously with parameters of pupillary reactivity [i.e., maximum and minimum apertures, light reflex latency (Lat), constriction and redilation velocities (CV, DV), and percentage change of apertures (per-change)] using a portable pupillometer (NeurOptics®). Computed tomography (CT) scans were analyzed to determine lesion location, size, intraventricular hemorrhage, hydrocephalus, midline shift, and compression or absence of the basal cisterns. We performed receiver operating characteristics analysis to investigate associations of ICP levels with pupillary parameters and to determine best cutoff values for prediction of ICP elevation. After dichotomization of assessments according to ICP values (normal: < 20 mmHg, elevated: ≥ 20 mmHg), prognostic performance of the determined cutoff parameters of pupillary function versus of CT-imaging findings was analyzed by calculating sensitivity, specificity, positive and negative predictive values (logistic regression, corresponding ORs with 95% CIs). Results In 23 patients (11 women, median age 59.0 (51.0–69.0) years), 1,934 assessments were available for analysis. A total of 74 ICP elevations ≥ 20 mmHg occurred in seven patients. Best discriminative thresholds for ICP elevation were: CV < 0.8 mm/s (AUC 0.740), per-change < 10% (AUC 0.743), DV < 0.2 mm/s (AUC 0.703), and Lat > 0.3 s (AUC 0.616). Positive predictive value of all four parameters to indicate ICP elevation ranged between 7.2 and 8.3% only and was similarly low for CT abnormalities (9.1%). We found high negative predictive values of pupillary parameters [CV: 99.2% (95% CI 98.3–99.6), per-change: 98.7% (95% CI 97.8–99.2), DV: 98.0% (95% CI 97.0–98.7), Lat: 97.0% (95% CI 96.0–97.7)], and CT abnormalities [99.7% (95% CI 99.2–99.9)], providing evidence that both techniques adequately identified ICH patients without ICP elevation. Conclusions Our data suggest an association between noninvasively detected changes in pupillary reactivity and ICP levels in sedated ICH patients. Although automated pupillometry and neuroimaging seem not sufficient to noninvasively indicate ICP elevation, both techniques, however, adequately identified ICH patients without ICP elevation. This finding may facilitate routine management by saving invasive ICP monitoring or repeated CT controls in patients with specific automated pupillometry readings.

Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling

BACKGROUND Despite advances in the treatment of poor-grade aneurysmal subarachnoid hemorrhage (aSAH), predicting the long-term outcome of aSAH remains challenging, although essential. OBJECTIVE To predict long-term outcomes after poor-grade aSAH using decision tree modeling. METHODS This was a retrospective analysis of a prospective multicenter observational registry of patients with poor-grade aSAH with a World Federation of Neurosurgical Societies (WFNS) grade IV or V. Outcome was assessed by the modified Rankin Scale (mRS) at 12 mo, and an unfavorable outcome was defined as an mRS of 4 or 5 or death. Long-term prognostic models were developed using multivariate logistic regression and decision tree algorithms. An additional independent testing dataset was collected for external validation. Overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curves (AUC) were used to assess model performance. RESULTS Of the 266 patients, 139 (52.3%) had an unfavorable outcome. Older age, absence of pupillary reactivity, lower Glasgow coma score (GCS), and higher modified Fisher grade were independent predictors of unfavorable outcome. Modified Fisher grade, pupillary reactivity, GCS, and age were used in the decision tree model, which achieved an overall accuracy of 0.833, sensitivity of 0.821, specificity of 0.846, and AUC of 0.88 in the internal test. There was similar predictive performance between the logistic regression and decision tree models. Both models achieved a high overall accuracy of 0.895 in the external test. CONCLUSION Decision tree model is a simple tool for predicting long-term outcomes after poor-grade aSAH and may be considered for treatment decision-making.

Quantitative Pupillometry in the Intensive Care Unit

Quantitative pupillometry provides a noninvasive and objective assessment within the neurological examination. This review details the physiology of the pupillary light response, the clinical significance of changes in pupillary reactivity, and the variables that compose the Neurological Pupil index or NPi are discussed. This article reviews the most recent applications and advances in quantitative pupillometry for noninvasive intracranial pressure monitoring, postcardiac arrest prognostication, and subarachnoid hemorrhage. Also discussed are the limitations and confounders of quantitative pupillometry in the modern neurological intensive care unit.