Global monitoring in the neurocritical care unit

Invasive Neuromonitoring in the Stroke Patient

With advances in technology, the options to manage patients with neurologic injuries are often complex. Critical care management of neurologic injury has historically focused on the prevention of secondary ischemic injury through aggressive management of intracranial pressure (ICP) and maintenance of adequate cerebral perfusion pressure (CPP). However, ICP monitoring alone does not identify ischemic changes that herald patient deterioration. Advocates of multimodality monitoring cite the value of early detection of changes in brain oxygenation levels and brain metabolism as advantageous in optimizing stroke outcomes. ICP monitoring alone should not be the sole source of information on which therapy is guided but should be incorporated into the arsenal of emerging and promising invasive neuromonitoring devices.

Quantitative pupillary light reflex assessment for prognosis of carbon monoxide poisoning

Background

A non-reactive pupil in standard pupillary light reflex (sPLR) is regarded as a factor predicting neurological sequelae at 1-month after carbon monoxide (CO) poisoning. An automated pupillometer is used in the intensive care unit to quantitatively assess PLR. Quantitative PLR (qPLR) was superior to sPLR using penlight for prognosis of various neurological diseases. Therefore, this study aimed to analyze whether quantitative pupillary variables (neurological Pupil index [NPi] and qPLR) are superior to sPLR in predicting 1-month neurocognitive sequelae after acute CO poisoning.

Methods

We performed a prospective observational study of consecutive patients with acute CO poisoning admitted to an emergency department (ED) between August 2019 and December 2020 in a single academic medical center. sPLR and pupillometer examinations (qPLR and NPi) were performed by emergency physicians at the ED on hospital days 0-2. The lowest values among those recorded within 24 h and during the total measurement period were considered the 24-h and total lowest values, respectively. Global Deterioration Scale scores were measured at 1 month as an outcome and were dichotomized into favorable (1-4) or poor (5-7) outcomes.

Results

We analyzed the data of 104 adult patients with acute CO poisoning. qPLR was significantly higher in the favorable outcome group than in the poor outcome group 24-h and total lowest values (21.2% vs. 15.0%, p = 0.006 and 21.0% vs. 14.8%, p = 0.006). qPLR <18% had fair predictive power for poor neurocognitive outcomes [area under the curve (AUC), 0.70; 95% confidence interval (0.60-0.78)]. Among the patients with decreased mental status (Glasgow Coma Scale ≤12), the power of NPi and qPLR increased [AUC, 0.72 and AUC, 0.80]. NPi < 1 and qPLR <18% showed sensitivity (9.5% vs. 76.2%) and specificity (98.8% vs. 67.5%) for the prediction of poor outcomes. qPLR was significantly superior to sPLR in predicting poor neurocognitive outcomes at 1 month after CO poisoning (p = 0.007).

Conclusion

qPLR and NPi were superior to sPLR in terms of predicting poor neurocognitive outcomes. qPLR and NPi measured from hospital days 0-2 may be valuable in predicting neurocognitive outcome.

Efficacy of Quantitative Pupillary Light Reflex for Predicting Neurological Outcomes in Patients Treated with Targeted Temperature Management after Cardiac Arrest: A Systematic Review and Meta-Analysis

Background and objectives: This study aims to evaluate the usefulness of the quantitative pupillary light reflex as a prognostic tool for neurological outcomes in post-cardiac arrest patients treated with targeted temperature management (TTM). Material and Methods: We systematically searched MEDLINE, EMBASE, and the Cochrane Library (search date: 9 July 2021) for studies on post-cardiac arrest patients treated with TTM that had measured the percent constriction of pupillary light reflex (%PLR) with quantitative pupillometry as well as assessed the neurological outcome. For an assessment of the methodological quality of the included studies, two authors utilized the prognosis study tool independently. Results: A total of 618 patients from four studies were included in this study. Standardized mean differences (SMDs) were calculated to compare patients with good or poor neurological outcomes. A higher %PLR measured at 0–24 h after hospital admission was related to good neurological outcomes at 3 months in post-cardiac arrest patients treated with TTM (SMD 0.87; 95% confidence interval 0.70–1.05; I² = 0%). A higher %PLR amplitude measured at 24–48 h after hospital admission was also associated with a good neurological outcome at 3 months in post-cardiac arrest patients treated with TTM, but with high heterogeneity (standardized mean difference 0.86; 95% confidence interval 0.40–1.32; I² = 70%). The evidence supporting these findings was of poor quality. For poor neurological outcome, the prognosis accuracy of %PLR was 9.19 (pooled diagnostic odds ratio, I² = 0%) and 0.75 (area under the curve). Conclusions: The present meta-analysis could not reveal that change of %PLR was an effective tool in predicting neurological outcomes for post-cardiac arrest patients treated with TTM owing to a paucity of included studies and the poor quality of the evidence.

Data-driven ICU management: Using Big Data and algorithms to improve outcomes

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic. In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care.

Nursing Interventions for Patients with Intracranial Hypertension: Integrative Literature Review

Objective: the study sought to identify, in national and international publications, the principal Nursing interventions aimed at patients with intracranial hypertension. Materials and Method: integrative literature review with search in LILACS, PubMed, Scopus, Web of Science, Cinahal, and Google Scholar databases, from 2013 to 2018. Results: the sample was comprised of seven articles fulfilling the inclusion criteria. Two thematic categories were established for the Nursing interventions aimed at patients with intracranial hypertension: cognitive skills and clinical reasoning, necessary to control neuro-physiological parameters and prevent intracranial hypertension, and evidence-based practices to improve care for neuro-critical patients. Conclusions: intracranial hypertension is an event of great clinical impact, whose complications can be minimized and control through specific Nursing interventions that encompass control of neuro-physiological and hemodynamic parameters and prevention of increased intracranial pressure related with the performance of procedures by the Nursing staff.

Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit

The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient's condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient's clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient's neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.

Quantitative assessment of pupillary light reflex for early prediction of outcomes after out-of-hospital cardiac arrest: A multicentre prospective observational study

AIM

To clarify whether quantitative assessment of pupillary light reflexes (PLR) can predict the outcome of post-cardiac arrest (CA) patients during the first 72 h after the return of spontaneous circulation (ROSC).

METHODS

Fifty adults resuscitated after non-traumatic out-of-hospital CA (OHCA) (mean age 64.1 years old, 36 males) were enrolled in four emergency hospitals. PLR was sequentially measured at 0, 6, 12, 24, 48, and 72 h after ROSC by an automated portable infrared pupillometry. PLR values for each time point were compared between both survivors and non-survivors, and patients with either favourable (Cerebral Performance Category (CPC) 1 or 2) or unfavourable neurological outcomes.

RESULTS

Twenty-three patients survived for 90 days after CA, and 13 patients achieved favourable neurological outcomes. The PLR values of the survivors and patients with favourable neurological outcomes were consistently greater than those of non-survivors (P < 0.001) and those with unfavourable neurological outcomes (P < 0.001), respectively. The change in PLR over time was not statistically different between the outcome groups. The 0-hour PLR best predicted both 90-day survival (AUC = 0.82, cutoff 3%, sensitivity 0.87, specificity 0.80) and favourable neurological outcomes (AUC = 0.84, cutoff 6%, sensitivity 0.92, specificity 0.74). No patient with a 6-hour PLR less than 3% survived for 90 days after CA.

CONCLUSIONS

Quantitatively measured PLR was consistently greater in survivors and patients with favourable neurological outcomes during the 72 h after ROSC. Quantitative assessment of PLR at as early as 0 h has a potential role for prognostication in post-CA patients.

Invasive Neuromonitoring

Advances in technology have resulted in a plethora of invasive neuromonitoring options for practitioners to manage while caring for the complex needs of the critical care patient. Although many types of invasive neuromonitoring are available to the practitioner, intraparenchymal monitors and external ventricular devices are used most frequently in the clinical setting and are the focus of this article. In addition, multimodality monitoring has been noted to confer a survival benefit in patients with this complex type of invasive neuromonitoring and is discussed as well.