Suppression-burst or a low voltage at 24 h after ROSC was not compatible with good outcome in this series. Normal background voltage without epileptiform discharges predicted a good outcome.

Continuous electroencephalographic (EEG) monitoring has become an invaluable tool for the assessment of brain function in critically ill patients. However, interpretation of EEG waveforms, especially in the intensive care unit (ICU) setting is fraught with ambiguity. The term ictal-interictal continuum encompasses EEG patterns that are potentially harmful and can cause neuronal injury. There are no clear guidelines on how to treat EEG patterns that lie on this continuum. We advocate the following approaches in a step wise manner: (1) identify and exclude clear electrographic seizures and status epilepticus (SE), i.e., generalized spike-wave discharges at 3/s or faster; and clearly evolving discharges of any type (rhythmic, periodic, fast activity), whether focal or generalized; (2) exclude clear interictal patterns, i.e., spike-wave discharges, periodic discharges, and rhythmic patterns at 1/s or slower with no evolution, unless accompanied by a clear clinical correlate, which would make them ictal regardless of the frequency; (3) consider any EEG patterns that lie in between the above two categories as being on the ictal-interictal continuum; (4) compare the electrographic pattern of the ictal-incterictal continuum to the normal background and unequivocal seizures (if present) from the same patient; (5) when available, correlate ictal-interictal continuum pattern with other markers of neuronal injury such as neuronal specific enolase (NSE) levels, brain imaging findings, depth electrode recordings, data from microdialysis, intracranial pressure fluctuations, and brain oxygen measurement; and (6) perform a diagnostic trial with preferably a nonsedating antiepileptic drug with the endpoint being both clinical and electrographic improvement. Minimize the use of anesthetics or multiple AEDs unless there is clear supporting evidence from ancillary tests or worsening of the EEG patterns over time, which could indicate possible neuronal injury.

Early onset PAM is not always associated with lack of recovery of consciousness. EEG can help discriminate between patients who may or may not regain consciousness by the time of hospital discharge.

Objective: Electroencephalogram (EEG) features predict neurological recovery following cardiac arrest. Recent work has shown that prognostic implications of some key EEG features change over time. We explore whether time dependence exists for an expanded selection of quantitative EEG (QEEG) features and whether accounting for this time-dependence enables better prognostic predictions. Design: Retrospective.

ObjectiveTo examine the prognostic ability of the combination of EEG and MRI in identifying patients with good outcome in postanoxic myoclonus (PAM) after cardiac arrest (CA).MethodsAdults with PAM who had an MRI within 20 days after CA were identified in 4 prospective CA registries. The primary outcome measure was coma recovery to command following by hospital discharge. Clinical examination included brainstem reflexes and motor activity. EEG was assessed for best background continuity, reactivity, presence of epileptiform activity, and burst suppression with identical bursts (BSIB). MRI was examined for presence of diffusion restriction or fluid-attenuated inversion recovery changes consistent with anoxic brain injury. A prediction model was developed using optimal combination of variables.ResultsAmong 78 patients, 11 (14.1%) recovered at discharge and 6 (7.7%) had good outcome (Cerebral Performance Category < 3) at 3 months. Patients who followed commands were more likely to have pupillary and corneal reflexes, flexion or better motor response, EEG continuity and reactivity, no BSIB, and no anoxic injury on MRI. The combined EEG/MRI variable of continuous background and no anoxic changes on MRI was associated with coma recovery at hospital discharge with sensitivity 91% (95% confidence interval [CI], 0.59–1.00), specificity 99% (95% CI, 0.92–1.00), positive predictive value 91% (95% CI, 0.59–1.00), and negative predictive value 99% (95% CI, 0.92–1.00).ConclusionsEEG and MRI are complementary and identify both good and poor outcome in patients with PAM with high accuracy. An MRI should be considered in patients with myoclonus showing continuous or reactive EEGs.

Evaluation of behavioral impairment during epileptic seizures is critical for medical decision-making, including accurate diagnosis, recommendations for driving and presurgical evaluation. We investigated the quality of behavioral testing during inpatient video-EEG monitoring at an established epilepsy center, and introduce a technical innovation that may improve clinical care. We retrospectively reviewed video-EEG data from 152 seizures in 33 adult or pediatric patients admitted for video-EEG monitoring. Behavioral testing with questions or commands was performed in only 50% of seizures ictally, 73% of seizures postictally, and 80% with either ictal or postical testing combined. Further, the questions or commands were highly inconsistent and were performed by non-medical personnel in about a quarter of cases. In an effort to improve this situation we developed and here introduce Automatic Responsiveness Testing in Epilepsy (ARTiE), a series of video-recorded behavioral tasks automatically triggered to play in the patient’s room by computerized seizure detection. In initial technical testing using pre-recorded or live video-EEG data we found that ARTiE is initiated reliably by automatic seizure detection. With additional clinical testing we hope that ARTiE will succeed in providing comprehensive and reliable behavioral evaluation during seizures for people with epilepsy to greatly improve their clinical care.

Objective To investigate the factors associated with the long‐term continuation of anti‐seizure medications (ASMs) in acute stroke patients. Methods We performed a retrospective cohort study of stroke patients with concern for acute symptomatic seizures (ASySs) during hospitalization who subsequently visited the poststroke clinic. All patients had continuous EEG (cEEG) monitoring. We generated a multivariable logistic regression model to analyze the factors associated with the primary outcome of continued ASM use after the first poststroke clinic visit. Results A total of 507 patients (43.4% ischemic stroke, 35.7% intracerebral hemorrhage, and 20.9% aneurysmal subarachnoid hemorrhage) were included. Among them, 99 (19.5%) suffered from ASySs, 110 (21.7%) had epileptiform abnormalities (EAs) on cEEG, and 339 (66.9%) had neither. Of the 294 (58%) patients started on ASMs, 171 (33.7%) were discharged on them, and 156 (30.3% of the study population; 53.1% of patients started on ASMs) continued ASMs beyond the first poststroke clinic visit [49.7 (±31.7) days after cEEG]. After adjusting for demographical, stroke‐ and hospitalization‐related variables, the only independent factors associated with the primary outcome were admission to the NICU [Odds ratio (OR) 0.37 (95% CI 0.15–0.9)], the presence of ASySs [OR 20.31(95% CI 9.45–48.43)], and EAs on cEEG [OR 2.26 (95% CI 1.14–4.58)]. Interpretation Almost a third of patients with poststroke ASySs concerns may continue ASMs for the long term, including more than half started on them acutely. Admission to the NICU may lower the odds, and ASySs (convulsive or electrographic) and EAs on cEEG significantly increase the odds of long‐term ASM use.

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