Non-Invasive Multimodal Neuromonitoring in Non-Critically Ill Hospitalized Adult Patients With COVID-19: A Systematic Review and Meta-Analysis

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study

BACKGROUND AND OBJECTIVES

Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG.

METHODS

A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study.

RESULTS

A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses.

DISCUSSION

2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

Stroke in critically ill patients with respiratory failure due to COVID-19: Disparities between low-middle and high-income countries

PURPOSE

We aimed to compare the incidence of stroke in low-and middle-income countries (LMICs) versus high-income countries (HICs) in critically ill patients with COVID-19 and its impact on in-hospital mortality.

METHODS

International observational study conducted in 43 countries. Stroke and mortality incidence rates and rate ratios (IRR) were calculated per admitted days using Poisson regression. Inverse probability weighting (IPW) was used to address the HICs vs. LMICs imbalance for confounders.

RESULTS

23,738 patients [20,511(86.4 %) HICs vs. 3,227(13.6 %) LMICs] were included. The incidence stroke/1000 admitted-days was 35.7 (95 %CI = 28.4-44.9) LMICs and 17.6 (95 %CI = 15.8-19.7) HICs; ischemic 9.47 (95 %CI = 6.57-13.7) LMICs, 1.97 (95 %CI = 1.53, 2.55) HICs; hemorrhagic, 7.18 (95 %CI = 4.73-10.9) LMICs, and 2.52 (95 %CI = 2.00-3.16) HICs; unspecified stroke type 11.6 (95 %CI = 7.75-17.3) LMICs, 8.99 (95 %CI = 7.70-10.5) HICs. In regression with IPW, LMICs vs. HICs had IRR = 1.78 (95 %CI = 1.31-2.42, p < 0.001). Patients from LMICs were more likely to die than those from HICs [43.6% vs 29.2 %; Relative Risk (RR) = 2.59 (95 %CI = 2.29-2.93), p < 0.001)]. Patients with stroke were more likely to die than those without stroke [RR = 1.43 (95 %CI = 1.19-1.72), p < 0.001)].

CONCLUSIONS

Stroke incidence was low in HICs and LMICs although the stroke risk was higher in LMICs. Both LMIC status and stroke increased the risk of death. Improving early diagnosis of stroke and redistribution of healthcare resources should be a priority.

TRIAL REGISTRATION

ACTRN12620000421932 registered on 30/03/2020.

Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

The importance of monitoring cerebral oxygenation in non brain injured patients

Over the past few years, the use of non-invasive neuromonitoring in non-brain injured patients has increased, as a result of the recognition that many of these patients are at risk of brain injury in a wide number of clinical scenarios and therefore may benefit from its application which allows interventions to prevent injury and improve outcome. Among these, are post cardiac arrest syndrome, sepsis, liver failure, acute respiratory failure, and the perioperative settings where in the absence of a primary brain injury, certain groups of patients have high risk of neurological complications. While there are many neuromonitoring modalities utilized in brain injured patients, the majority of those are either invasive such as intracranial pressure monitoring, require special skill such as transcranial Doppler ultrasonography, or intermittent such as pupillometry and therefore unable to provide continuous monitoring. Cerebral oximetry using Near infrared Spectroscopy, is a simple non invasive continuous measure of cerebral oxygenation that has been shown to be useful in preventing cerebral hypoxemia both within the intensive care unit and the perioperative settings. At present, current recommendations for standard monitoring during anesthesia or in the general intensive care concentrate mainly on hemodynamic and respiratory monitoring without specific indications regarding the brain, and in particular, brain oximetry. The aim of this manuscript is to provide an up-to-date overview of the pathophysiology and applications of cerebral oxygenation in non brain injured patients as part of non-invasive multimodal neuromonitoring in the early identification and treatment of neurological complications in this population.

The value of EEG attenuation in the prediction of outcome in COVID-19 patients

Introduction

During the COVID-19 pandemic, electroencephalography (EEG) proved to be a useful tool to demonstrate brain involvement. Many studies reported non-reactive generalized slowing as the most frequent pattern and epileptiform activity in a minority of patients.

Objective

To investigate the prevalence of diffuse unreactive background attenuation or suppression and its correlation with outcome in a cohort of COVID-19 patients.

Methods

The EEGs recorded during the first year of the COVID-19 pandemic were retrospectively evaluated to identify the main pattern and focus on the occurrence of a low-voltage background, either attenuated (10–20 μV) or suppressed (< 10 μV). We sought a correlation between in-hospital mortality and low-voltage EEG. In a subsample of patients, biomarkers of inflammation, hypoxemia and organ failure were collected. Brain imaging was also evaluated.

Results

Among 98 EEG performed in 50 consecutive patients, diffuse unreactive slowing was the most prevalent pattern (54%), followed by unreactive attenuation or suppression pattern (26%), being the latter significantly correlated with an unfavourable outcome (p = 0.0004). Survivors showed significantly lower interleukine-6 values compared to non-survivors. Patients with attenuated EEG and non-survivors also showed lower PaO₂/FiO₂ values. Neuroradiological findings were very heterogeneous with a prevalence of lesions suggestive of a microangiopathic substrate.

Conclusions

EEG attenuation or suppression may be more frequent than previously reported and significantly associated with a poor outcome. SARS-CoV-2 infection may result in encephalopathy and reduced EEG voltage through mechanisms that are still unknown but deserve attention given its negative impact on prognosis.