Continuous EEG Monitoring Predicts a Clinically Meaningful Recovery Among Adult Inpatients:

Developing a Standardized Approach to Grading the Level of Brain Dysfunction on EEG

PURPOSE

To assess variability in interpretation of electroencephalogram (EEG) background activity and qualitative grading of cerebral dysfunction based on EEG findings, including which EEG features are deemed most important in this determination.

METHODS

A web-based survey (Qualtrics) was disseminated to electroencephalographers practicing in institutions participating in the Critical Care EEG Monitoring Research Consortium between May 2017 and August 2018. Respondents answered 12 questions pertaining to their training and EEG interpretation practices and graded 40 EEG segments (15-second epochs depicting patients' most stimulated state) using a 6-grade scale. Fleiss' Kappa statistic evaluated interrater agreement.

RESULTS

Of 110 respondents, 78.2% were attending electroencephalographers with a mean of 8.3 years of experience beyond training. Despite 83% supporting the need for a standardized approach to interpreting the degree of dysfunction on EEG, only 13.6% used a previously published or an institutional grading scale. The overall interrater agreement was fair ( k = 0.35). Having Critical Care EEG Monitoring Research Consortium nomenclature certification (40.9%) or EEG board certification (70%) did not improve interrater agreement ( k = 0.26). Predominant awake frequencies and posterior dominant rhythm were ranked as the most important variables in grading background dysfunction, followed by continuity and reactivity.

CONCLUSIONS

Despite the preference for a standardized grading scale for background EEG interpretation, the lack of interrater agreement on levels of dysfunction even among experienced academic electroencephalographers unveils a barrier to the widespread use of EEG as a clinical and research neuromonitoring tool. There was reasonable agreement on the features that are most important in this determination. A standardized approach to grading cerebral dysfunction, currently used by the authors, and based on this work, is proposed.

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.

Does continuous electroencephalography influence therapeutic decisions in neurocritical care?

OBJECTIVES

In the neurocritical care unit (neuro-ICU), the impact of continuous EEG (cEEG) on therapeutic decisions and prognostication, including outcome prediction using the Status Epilepticus Severity Score (STESS), is poorly investigated. We studied to what extent cEEG contributes to treatment decisions, and how this relates to clinical outcome and the use of STESS in neurocritical care.

METHODS

We included patients admitted to the neuro-ICU or neurological step-down unit of a tertiary referral hospital between 05/2013 and 06/2015. Inclusion criteria were ≥20 h of cEEG monitoring and age ≥15 years. Exclusion criteria were primary epileptic and post-cardiac arrest encephalopathies.

RESULTS

Ninety-eight patients met inclusion criteria, 80 of which had status epilepticus, including 14 with super-refractory status. Median length of cEEG monitoring was 50 h (range 21-374 h). Mean STESS was lower in patients with favorable outcome 1 year after discharge (modified Rankin Scale [mRS] 0-2) compared to patients with unfavorable outcome (mRS 3-6), albeit not statistically significant (mean STESS 2.3 ± 2.1 vs 3.6 ± 1.7, p = 0.09). STESS had a sensitivity of 80%, a specificity of 42%, and a negative predictive value of 93% for outcome. cEEG results changed treatment decisions in 76 patients, including escalation of antiepileptic treatment in 65 and reduction in 11 patients.

CONCLUSION

Status Epilepticus Severity Score had a high negative predictive value but low sensitivity, suggesting that STESS should be used cautiously. Of note, cEEG results altered clinical decision-making in three of four patients, irrespective of the presence or absence of status epilepticus, confirming the clinical value of cEEG in neurocritical care.

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.

Drug Trials in Status Epilepticus: Current Evidence and Future Concepts

Status epilepticus (SE) is a neurologic emergency with high morbidity and mortality. After many advances in the field, several unanswered questions remain for optimal treatment after the early stage of SE. This narrative review describes some of the important drug trials for SE treatment that have shaped the understanding of the treatment of SE. The authors also propose possible clinical trial designs for the later stages of SE that may allow assessment of currently available and new treatment options. Status epilepticus can be divided into four stages for treatment purposes: early, established, refractory, and superrefractory. Ongoing convulsive seizures for more than 5 minutes or nonconvulsive seizure activity for more than 10 to 30 minutes is considered early SE. Failure to control the seizure with first-line treatment (usually benzodiazepines) is defined as established SE. If SE continues despite treatment with an antiseizure medicine, it is considered refractory SE, which is usually treated with additional antiseizure medicines or intravenous anesthetic agents. Continued seizures for more than 24 hours despite use of intravenous anesthetic agents is termed superrefractory SE. Evidence-based treatment recommendations from high-quality clinical trials are available for only the early stages of SE. Among the challenges for designing a treatment trial for the later stages SE is the heterogeneity of semiology, etiology, age groups, and EEG correlates. In many instances, SE is nonconvulsive in later stages and diagnosis is possible only with EEG. EEG patterns can be challenging to interpret and only recently have consensus criteria for EEG diagnosis of SE emerged. Despite having these EEG criteria, interrater agreement in EEG interpretation can be challenging. Defining successful treatment can also be difficult. Finally, the ethics of randomizing treatment and possibly using a placebo in critically ill patients must also be considered. Despite these challenges, clinical trials can be designed that navigate these issues and provide useful answers for how best to treat SE at various stages.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

OBJECTIVE

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification.

METHODS

IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application.

RESULTS

Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%.

CONCLUSION

This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers.

SIGNIFICANCE

This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.