Edilberto Amorim

Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic–ischemic brain injury

OBJECTIVE Hypoxic brain injury is the largest contributor to disability and mortality after cardiac arrest. We aim to identify electroencephalogram (EEG) characteristics that can predict outcome on cardiac arrest patients treated with targeted temperature management (TTM). METHODS We retrospectively examined clinical, EEG, functional outcome at discharge, and in-hospital mortality for 373 adult subjects with return of spontaneous circulation after cardiac arrest. Poor outcome was defined as a Cerebral Performance Category score of 3-5. Pure suppression-burst (SB) was defined as SB not associated with status epilepticus (SE), seizures, or generalized periodic discharges. RESULTS In-hospital mortality was 68.6% (N=256). Presence of both unreactive EEG background and SE was associated with a positive predictive value (PPV) of 100% (95% confidence interval: 0.96-1) and a false-positive rate (FPR) of 0% (95% CI: 0-0.11) for poor functional outcome. A prediction model including demographics data, admission exam, presence of status epilepticus, pure SB, and lack of EEG reactivity had an area under the curve of 0.92 (95% CI: 0.87-0.95) for poor functional outcome prediction, and 0.96 (95% CI: 0.94-0.98) for in-hospital mortality. Presence of pure SB (N=87) was confounded by anesthetics use in 83.9% of the cases, and was not an independent predictor of poor functional outcome, having a FPR of 23% (95% CI: 0.19-0.28). CONCLUSIONS An unreactive EEG background and SE predicted poor functional outcome and in-hospital mortality in cardiac arrest patients undergoing TTM. Prognostic value of pure SB is confounded by use of sedative agents, and its use on prognostication decisions should be made with caution.

Performance of Spectrogram-Based Seizure Identification of Adult EEGs by Critical Care Nurses and Neurophysiologists

Purpose: Continuous EEG screening using spectrograms or compressed spectral arrays (CSAs) by neurophysiologists has shorter review times with minimal loss of sensitivity for seizure detection when compared with visual analysis of raw EEG. Limited data are available on the performance characteristics of CSA-based seizure detection by neurocritical care nurses. Methods: This is a prospective cross-sectional study that was conducted in two academic neurocritical care units and involved 33 neurointensive care unit nurses and four neurophysiologists. Results: All nurses underwent a brief training session before testing. Forty two-hour CSA segments of continuous EEG were reviewed and rated for the presence of seizures. Two experienced clinical neurophysiologists masked to the CSA data performed conventional visual analysis of the raw EEG and served as the gold standard. The overall accuracy was 55.7% among nurses and 67.5% among neurophysiologists. Nurse seizure detection sensitivity was 73.8%, and the false-positive rate was 1-per-3.2 hours. Sensitivity and false-alarm rate for the neurophysiologists was 66.3% and 1-per-6.4 hours, respectively. Interrater agreement for seizure screening was fair for nurses (Gwet AC1 statistic: 43.4%) and neurophysiologists (AC1: 46.3%). Conclusions: Training nurses to perform seizure screening utilizing continuous EEG CSA displays is feasible and associated with moderate sensitivity. Nurses and neurophysiologists had comparable sensitivities, but nurses had a higher false-positive rate. Further work is needed to improve sensitivity and reduce false-alarm rates.

An enhanced cerebral recovery index for coma prognostication following cardiac arrest.

Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27.

S96. EEG reactivity testing practices for cardiac arrest coma prognostication in North America

Introduction Lack of EEG reactivity is associated with unfavorable functional outcome in hypoxic-ischemic brain injury. Recent literature suggests that EEG reactivity inter-rater agreement between experts is as low as 26–50%. It is unclear if technical procedures, nomenclature variability, and practice patterns might contribute to poor inter-rater agreement rates. Methods We conducted a survey of representatives from North American institutions participating in the Critical Care EEG Monitoring Research Consortium to assess practice patterns involving EEG reactivity testing. The 10-question multiple choice survey evaluated center-specific metrics related to technical, personnel, and procedural aspects of EEG reactivity testing and assessment specific to cardiac arrest prognostication. One response per institution was obtained. Results We received responses from 25 institutions, including seven pediatric hospitals. A standardized EEG reactivity testing protocol was available in 44% of centers and 44% of respondents felt that reactivity assessment was subjective. Electroencephalogram reactivity testing usually started during hypothermia (76%) and tipically happened at least once daily (63%). Auditory stimulation and nail bed pressure were the most frequently used stimulation modalities (83% and 63% respectively). One institution included nipple pinching as part of its testing protocol. Stimulation was performed primarily by EEG Technologists (76%). Video-EEG assessment was the most common review method (92%). Changes in EEG amplitude were not considered consistent with EEG reactivity by 24% of centers. Stimulus induced rhythmic, periodic, or ictal discharges were considered indicative of EEG reactivity in 44% of centers. Conclusion There is substantial variability in EEG reactivity testing practices among North American academic medical centers. Lack of standardization of stimulation protocols, training of EEG Technologists, and ambiguous definitions of EEG reactivity might contribute to subjectivity and variability in EEG reactivity assessment.

F91. Early quantitative EEG reactivity predicts 6-month outcome in hypoxic ischemic brain injury

Introduction Early EEG background reactivity is a strong predictor of neurological recovery after hypoxic-ischemic brain injury despite hypothermia and sedation. Unfortunately, expert interrater-agreement on visual scoring of EEG background reactivity ranges from 24% to 50%. Recent studies indicate that machine-learning approaches using quantitative EEG (QEEG) might yield equivalent or superior performance to current EEG reactivity assessment practices, however its ability to predict outcomes has not been tested. We hypothesized that a QEEG reactivity method can predict long-term functional outcome in hypoxic ischemic brain injury. Methods We retrospectively reviewed clinical and EEG data of cardiac arrest patients managed with hypothermia at two university hospitals. EEG reactivity was tested daily using a structured exam consisting of auditory, tactile, and visual stimulation. Our quantitative EEG method evaluated changes in EEG spectra, entropy, and frequency features during 30 s before and after each stimulation-step (30 QEEG features used). Only the first EEG reactivity assessment for each subject was used in the final analysis. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. A penalized multinomial logistic regression was utilized for feature selection and a random-forest classifier was employed in the training and validation sets. Model performance evaluation metric was the area under the curve. Results Outcome and EEG data was available for a total 77 subjects, and 30 cases were excluded due to presence of burst-suppression, periodic epileptiform discharges, or EEG artifact. Forty-seven subjects were included in the final analysis. Mean age was 57.8 (standard deviation 17.9) years and 29.8% had good outcome. The combination of four features provided best outcome prediction performance with an AUC of 0.87 (Kolmogorov-Smirnov test, skewness, Two-group test, and Renyi entropy). Conclusion Early QEEG reactivity is predictive of good outcome at six months. A quantitative approach to EEG reactivity analysis might facilitate accurate and individualized prognostication in hypoxic-ischemic brain injury.

S85. Automated quantitative EEG reactivity testing using actigraphy monitoring in cardiac arrest coma prognostication

Introduction EEG background reactivity is a strong predictor of coma recovery after cardiac arrest. The current clinical value of EEG background reactivity testing is limited by inadequate inter-rater expert agreement, few number of daily assessments, and unsuitability for quantitative tracking by visual review. We hypothesized that a quantitative EEG reactivity method using actigraphy-triggered events from a wrist-worn wearable can predict long-term functional outcome in comatose cardiac arrest patients treated with targeted temperature management. Methods We prospectively recorded clinical, actigraphy, and EEG data of comatose cardiac arrest patients managed with targeted temperature management at a single U.S. academic hospital. Continuous actigraphy data obtained from a wrist-worn wearable (Affectiva 3) was synchronized to continuous EEG data for up to 96 h after cardiac arrest. Change in actigraphy across time was used as a surrogate of bedside external physical stimulation (i.e. actigraphy-triggered event). Our quantitative EEG method evaluated changes in EEG spectra, entropy, and frequency features during 30 s before and 30 s after each actigraphy-triggered event (46 EEG features used). Actigraphy threshold and EEG window duration (pre and post actigraphy-triggered events) were utilized as hyperparameters in the model. In addition to actigraphy-triggered quantitative EEG background reactivity, EEG reactivity was scored visually as present or absent once daily by a single expert electroencephalographer during clinical care. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. Classification was carried out using extreme learning machine and leave-one-subject-out cross validation. Final outcome class prediction for each individual subject was determined using simple majority voting across all actigraphy-triggered events. Results Ten subjects were monitored. Mean age was 60 (standard deviation (SD) ± 14.6) years, 10% were female, 50% had a shockable rhythm, and 40% had good outcome at six months. A total of 19 (SD ± 8.9) actigraphy-triggered events per patient were detected. Best outcome prediction performance was achieved using 50-s-long EEG window (25-s pre and 25-s post each actigraphy-triggered event). The quantitative EEG reactivity method correctly predicted good outcome in 80% of cases (sensitivity and specificity 80%). Expert rating using once daily visual EEG reactivity assessments had 60% accuracy for good outcome prediction. Conclusion Quantitative EEG reactivity assessments using actigraphy-based triggered events is feasible and may support long-term outcome prediction in cardiac arrest coma. Integration of EEG and wearable sensors using machine learning methods might facilitate the deployment of dynamic multi-modal patient monitoring in the intensive care unit environment.

Seizures and Quantitative EEG

Continuous electroencephalogram (cEEG) monitoring has become increasingly common in ICU patients as nonconvulsive seizures and nonconvulsive status epilepticus are more widely recognized. Timely seizure recognition is important for clinical care and time-sensitive interventions. However, continuous EEG monitoring requires time-consuming review by expert neurophysiologists, which is difficult when resources are limited. As cEEG utilization has grown, there has been growing interest in quantitative methods by which to speed-up and enhance conventional visual review. In this chapter, we review various quantitative methods that have been developed to evaluate EEG signals (qEEG). We focus particularly on time-frequency representations of EEG data, known as “compressed spectral arrays” (CSAs) or simply spectrograms. We describe the basic theory behind spectral analysis and spectral estimation and how features of pathological EEG patterns, sharpness, and rhythmicity are reflected in spectrograms. We illustrate the theory with synthetically generated signals and with multiple real clinical cases. We propose a novel terminology by which to categorize the most commonly recurring CSA patterns. Finally, we touch on the clinical implications of using qEEG methods and other non-seizure-related uses of qEEG.