Does Continuous Video-EEG in Patients With Altered Consciousness Improve Patient Outcome? Current Evidence and Randomized Controlled Trial Design:

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine.

Continuous versus routine EEG in patients after cardiac arrest-Analysis of a randomized controlled trial (CERTA) - RESUS-D-22-00369

BACKGROUND

Electroencephalography (EEG) is essential to assess prognosis in patients after cardiac arrest (CA). Use of continuous EEG (cEEG) is increasing in critically-ill patients, but it is more resource-consuming than routine EEG (rEEG). Observational studies did not show a major impact of cEEG versus rEEG on outcome, but randomized studies are lacking.

METHODS

We analyzed data of the CERTA trial (NCT03129438), including comatose adults after CA undergoing cEEG (30-48 hours) or two rEEG (20-30 minutes each). We explored correlations between recording EEG type and mortality (primary outcome), or Cerebral Performance Categories (CPC, secondary outcome), assessed blindly at 6 months, using uni- and multivariable analyses (adjusting for other prognostic variables showing some imbalance across groups).

RESULTS

We analyzed 112 adults (52 underwent rEEG, 60 cEEG,); 31 (27.7%) were women; 68 (60.7%) patients died. In univariate analysis, mortality (rEEG 59%, cEEG 65%, p=0.318) and good outcome (CPC 1-2; rEEG 33%, cEEG 27%, p=0.247) were comparable across EEG groups. This did not change after multiple logistic regressions, adjusting for shockable rhythm, time to return of spontaneous circulation, serum neuron-specific enolase, EEG background reactivity, regarding mortality (rEEG vs cEEG: OR 1.60, 95% CI 0.43 - 5.83, p=0.477), and good outcome (OR 0.51, 95% CI 0.14 - 1.90, p=0.318).

CONCLUSION

This analysis suggests that cEEG or repeated rEEG are related to comparable outcomes of comatose patients after CA. Pending a prospective, large randomized trial, this finding does not support the routine use of cEEG for prognostication in this setting. Trial registration Continuous EEG Randomized Trial in Adults (CERTA); NCT03129438; July 25, 2019.

EEG spindles integrity in critical care adults. Analysis of a randomized trial

OBJECTIVES

Occurrence of EEG spindles has been recently associated with favorable outcome in ICU patients. Available data mostly rely on relatively small patients' samples, particular etiologies, and limited variables ascertainment. We aimed to expand previous findings on a larger dataset, to identify clinical and EEG patterns correlated with spindle occurrence, and explore its prognostic implications.

METHODS

Retrospective observational study of prospectively collected data from a randomized trial (CERTA, NCT03129438) assessing the relationship of continuous (cEEG) versus repeated routine EEG (rEEG) with outcome in adults with acute consciousness impairment. Spindles were prospectively assessed visually as 12-16Hz activity on fronto-central midline regions, at any time during EEG interventions. Uni- and multivariable analyses explored correlations between spindles occurrence, clinical and EEG variables, and outcome (modified Rankin Scale, mRS; mortality) at 6 months.

RESULTS

Among the analyzed 364 patients, spindles were independently associated with EEG background reactivity (OR 13.2, 95% CI: 3.11-56.26), and cEEG recording (OR 4.35, 95% CI: 2.5 - 7.69). In the cEEG subgroup (n=182), 33.5% had spindles. They had better FOUR scores (p=0.004), fewer seizures or status epilepticus (p=0.02), and lower mRS (p=0.02). Mortality was reduced (p=0.002), and independently inversely associated with spindle occurrence (OR 0.50, CI 95% 0.25-0.99) and increased EEG background continuity (OR 0.16, 95% CI: 0.07 - 0.41).

CONCLUSIONS

Besides confirming that spindle activity occurs in up to one third of acutely ill patients and is associated with better outcome, this study shows that cEEG has a higher yield than rEEG in identifying them. Furthermore, it unravels associations with several clinical and EEG features in this clinical setting.

Epilepsy in prolonged disorders of consciousness: a systematic review

To date, no guideline exists for the management of epilepsy in patients with prolonged Disorders of Consciousness (DoC). This review aimed to assess the occurrence of epilepsy and epileptic abnormalities (EA) in these patients, to determine their impact on recovery; and to review the effect of antiepileptic drugs (AED) and therapeutic interventions on seizure occurrence and consciousness recovery. A structured search for studies on prolonged DoC and epilepsy was undertaken following PRISMA guidelines. From an initial search resulting in 5,775 titles, twelve studies met inclusion criteria. The occurrence of epilepsy and EA in DoC was poorly and inconsistently reported across studies. The results estimated a seizure prevalence of 27% in DoC. No conclusive data were found for the effects of AED on recovery nor on the influence of any therapeutic interventions on seizure occurrence. Given the scarcity of data, it is premature to make evidence-based recommendations on epilepsy in prolonged DoC. Based on this review and current clinical practices the following are recommended: (1) repeated standard EEG for detecting seizures and EA; (2) treating epilepsy while avoiding AEDs with sedating or cognitive side-effects. Future research should use standardized classification systems for seizures and EA.

Electroencephalography of mechanically ventilated patients at high risk of delirium

Objective

Neurophysiological exploration of ICU delirium is limited. Here, we examined EEG characteristics of medical‐surgical critically ill patients with new‐onset altered consciousness state at high risk for ICU delirium.

Materials and methods

Pre‐planned analysis of non‐neurological mechanically ventilated medical‐surgical ICU subjects, who underwent a prospective multicenter randomized, controlled EEG study (NCT03129438, April 2017–November 2018). EEG characteristics, according to the 2012 ACNS nomenclature, included background activity, rhythmic periodic patterns/epileptic activity, amplitude, frequency, stimulus‐induced discharges, triphasic waves, reactivity, and NREM sleep. We explored EEG findings in delirious versus non‐delirious patients, specifically focusing on the presence of burst‐suppression and rhythmic periodic patterns (ictal‐interictal continuum), and ictal activity.

Results

We analyzed 91 patients (median age, 66 years) who underwent EEG because of new‐onset altered consciousness state at a median 5 days from admission; 42 patients developed delirium (46%). Burst‐suppression (10 vs 0%, p = .02), rhythmic/periodic patterns (43% vs 22%, p = .03) and epileptiform activity (7 vs 0%, p = .05) were more frequent in delirious versus non‐delirious patients. The presence of at least one of these abnormal EEG findings (32/91 patients; 35%) was associated with a significant increase in the likelihood of delirium (42 vs 15%, p = .006). Cumulative dose of sedatives and analgesics, as well as all other EEG characteristics, did not differ significantly between the two groups.

Conclusion

In mechanically ventilated non‐neurological critically ill patients with new‐onset alteration of consciousness, EEG showing burst‐suppression, rhythmic or periodic patterns, or seizures/status epilepticus indicate an increased risk of ICU delirium.

Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial

Question

In patients with acute consciousness impairment and no recent seizures, does continuous electroencephalogram (cEEG) correlate with reduced mortality compared with repeated routine EEG (rEEG)?

Findings

In this pragmatic, multicenter randomized clinical trial analyzing 364 adults, cEEG translated into a higher rate of seizures/status epilepticus detection and antiseizure treatment modifications but did not improve mortality compared with rEEG.

Meaning

Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Importance

In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection, but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome.

Objective

To assess whether cEEG is associated with reduced mortality compared with rEEG.

Design, Setting, and Participants

The pragmatic multicenter Continuous EEG Randomized Trial in Adults (CERTA) was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions.The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent.

Interventions

Participants were randomized 1:1 to cEEG for 30 to 48 hours vs 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines.

Main Outcomes and Measures

Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy.

Results

We analyzed 364 patients (33% women; mean [SD] age, 63 [15] years). At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR], 1.02; 95% CI, 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR, 1.26; 95% CI, 1.08-1.15; P = .004 and 3.37; 95% CI, 1.63-7.00; P = .001, respectively) and more frequent adaptations in antiseizure therapy (RR, 1.84; 95% CI, 1.12-3.00; P = .01).

Conclusions and Relevance

This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of antiseizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Trial Registration

ClinicalTrials.gov Identifier: NCT03129438

EEG in the Pediatric Intensive Care Unit: An Irish Experience

INTRODUCTION

Evidence for continuous EEG monitoring in the pediatric intensive care unit (PICU) is increasing. However, 24/7 access to EEG is not routinely available in most centers, and clinical management is often informed by more limited EEG resources. The experience of EEG was reviewed in a tertiary PICU where 24/7 EEG cover is unavailable.

METHODS

Retrospective EEG and clinical review of 108 PICU patients. Correlations were carried out between EEG and clinical variables including mortality. The role of EEG in clinical decision making was documented.

RESULTS

One hundred ninety-six EEGs were carried out in 108 PICU patients over 2.5 years (434 hours of recording). After exclusion of 1 outlying patient with epileptic encephalopathy, 136 EEGs (median duration, 65 minutes; range, 20 minutes to 4 hours 40 minutes) were included. Sixty-two patients (57%) were less than 12 months old. Seizures were detected in 18 of 107 patients (17%); 74% of seizures were subclinical; 72% occurred within the first 30 minutes of recording. Adverse EEG findings were associated with high mortality. Antiepileptic drug use was high in the studied population irrespective of EEG seizure detection. Prevalence of epileptiform discharges and EEG seizures diminished with increasing levels of sedation.

CONCLUSIONS

EEG provides important diagnostic information in a large proportion of PICU patients. In the absence of 24/7 EEG availability, empirical antiepileptic drug utilization is high.

What's new on EEG monitoring in the ICU

Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.

Informed consent in critically ill adults participating to a randomized trial

OBJECTIVE

The 2014 update of the Swiss law on research increases patients' protection; it adds specific requirements for emergency situations, implying an active search for patients' wishes regarding research participation; the possibility of consent waivers is not clearly stated. We explored its practical impact in a RCT on critically ill adults.

METHODS

We considered prospectively collected consents of a multicenter trial addressing the impact of continuous EEG on survival. We assessed the proportions of consents obtained strictly according to the law, of specific waivers for this study obtained from the IRB (early death; relatives' unavailability despite repeated attempts), and the yield of retrieving statements on willingness to research participation. We compared the proportion of consent refusals with those of recent trials in similar environments, and estimated the potential impact on study results.

RESULTS

Of 402 recruited patients, six had double inclusions, one died before intervention, and 27 (6.7%, alive on long-term) were excluded following consent refusal or withdrawal, leaving 368 analyzable patients. Specific waivers allowed inclusion of 134 (36.4%) patients, while informed consents were obtained for all others. A statement of willingness to research participation was found in only 14.1%. In recent trials, consent refusal oscillated between 0%-23%, according to different waiver policies.

CONCLUSIONS

Consent waivers should be specifically foreseen to prevent losing a potentially relevant proportion of patients reaching endpoints, and ensure results generalizability. The yield of looking for willingness to research participation seems low; this questions its current usefulness and calls for a public awareness campaign.

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

BACKGROUND

Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI).

METHOD

Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2).

RESULTS

The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance.

CONCLUSIONS

While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.