Patterns of antiseizure medication utilization in the Human Epilepsy Project.

OBJECTIVE: This study was undertaken to ascertain the natural history and patterns of antiseizure medication (ASM) use in newly diagnosed focal epilepsy patients who were initially started on monotherapy. METHODS: The data were derived from the Human Epilepsy Project. Differences between the durations of the most commonly first prescribed ASM monotherapies were assessed using a Cox proportional hazards model. Subjects were classified into three groups: monotherapy, sequential monotherapy, and polytherapy. RESULTS: A total of 443 patients were included in the analysis, with a median age of 32 years (interquartile range [IQR] = 20-44) and median follow-up time of 3.2 years (IQR = 2.4-4.2); 161 (36.3%) patients remained on monotherapy with their initially prescribed ASM at the time of their last follow-up. The mean (SEM) and median (IQR) duration that patients stayed on monotherapy with their initial ASM was 2.1 (2.0-2.2) and 1.9 (.3-3.5) years, respectively. The most commonly prescribed initial ASM was levetiracetam (254, 57.3%), followed by lamotrigine (77, 17.4%), oxcarbazepine (38, 8.6%), and carbamazepine (24, 5.4%). Among those who did not remain on the initial monotherapy, 167 (59.2%) transitioned to another ASM as monotherapy (sequential monotherapy) and 115 (40.8%) ended up on polytherapy. Patients remained significantly longer on lamotrigine (mean = 2.8 years, median = 3.1 years) compared to levetiracetam (mean = 2.0 years, median = 1.5 years) as a first prescribed medication (hazard ratio = 1.5, 95% confidence interval = 1.0-2.2). As the study progressed, the proportion of patients on lamotrigine, carbamazepine, and oxcarbazepine as well as other sodium channel agents increased from a little more than one third (154, 34.8%) of patients to more than two thirds (303, 68.4%) of patients. SIGNIFICANCE: Slightly more than one third of focal epilepsy patients remain on monotherapy with their first prescribed ASM. Approximately three in five patients transition to monotherapy with another ASM, whereas approximately two in five end up on polytherapy. Patients remain on lamotrigine for a longer duration compared to levetiracetam when it is prescribed as the initial monotherapy.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Machine Learning to Classify Relative Seizure Frequency From Chronic Electrocorticography.

PURPOSE: Brain responsive neurostimulation (NeuroPace) treats patients with refractory focal epilepsy and provides chronic electrocorticography (ECoG). We explored how machine learning algorithms applied to interictal ECoG could assess clinical response to changes in neurostimulation parameters. METHODS: We identified five responsive neurostimulation patients each with ≥200 continuous days of stable medication and detection settings (median, 358 days per patient). For each patient, interictal ECoG segments for each month were labeled as "high" or "low" to represent relatively high or low long-episode (i.e., seizure) count compared with the median monthly long-episode count. Power from six conventional frequency bands from four responsive neurostimulation channels were extracted as features. For each patient, five machine learning algorithms were trained on 80% of ECoG, then tested on the remaining 20%. Classifiers were scored by the area-under-the-receiver-operating-characteristic curve. We explored how individual circadian cycles of seizure activity could inform classifier building. RESULTS: Support vector machine or gradient boosting models achieved the best performance, ranging from 0.705 (fair) to 0.892 (excellent) across patients. High gamma power was the most important feature, tending to decrease during low-seizure-frequency epochs. For two subjects, training on ECoG recorded during the circadian ictal peak resulted in comparable model performance, despite less data used. CONCLUSIONS: Machine learning analysis on retrospective background ECoG can classify relative seizure frequency for an individual patient. High gamma power was the most informative, whereas individual circadian patterns of seizure activity can guide model building. Machine learning classifiers built on interictal ECoG may guide stimulation programming.

Mood and Anxiety Disorders and Suicidality in Patients With Newly Diagnosed Focal Epilepsy: An Analysis of a Complex Comorbidity.

BACKGROUND AND OBJECTIVES: Mood, anxiety disorders, and suicidality are more frequent in people with epilepsy than in the general population. Yet, their prevalence and the types of mood and anxiety disorders associated with suicidality at the time of the epilepsy diagnosis are not established. We sought to answer these questions in patients with newly diagnosed focal epilepsy and to assess their association with suicidal ideation and attempts. METHODS: The data were derived from the Human Epilepsy Project study. A total of 347 consecutive adults aged 18-60 years with newly diagnosed focal epilepsy were enrolled within 4 months of starting treatment. The types of mood and anxiety disorders were identified with the Mini International Neuropsychiatric Interview, whereas suicidal ideation (lifetime, current, active, and passive) and suicidal attempts (lifetime and current) were established with the Columbia Suicidality Severity Rating Scale (CSSRS). Statistical analyses included the t test, χ2 statistics, and logistic regression analyses. RESULTS: A total of 151 (43.5%) patients had a psychiatric diagnosis; 134 (38.6%) met the criteria for a mood and/or anxiety disorder, and 75 (21.6%) reported suicidal ideation with or without attempts. Mood (23.6%) and anxiety (27.4%) disorders had comparable prevalence rates, whereas both disorders occurred together in 43 patients (12.4%). Major depressive disorders (MDDs) had a slightly higher prevalence than bipolar disorders (BPDs) (9.5% vs 6.9%, respectively). Explanatory variables of suicidality included MDD, BPD, panic disorders, and agoraphobia, with BPD and panic disorders being the strongest variables, particularly for active suicidal ideation and suicidal attempts. DISCUSSION: In patients with newly diagnosed focal epilepsy, the prevalence of mood, anxiety disorders, and suicidality is higher than in the general population and comparable to those of patients with established epilepsy. Their recognition at the time of the initial epilepsy evaluation is of the essence.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Evaluation and Treatment of Seizures and Epilepsy During the COVID-19 Pandemic.

PURPOSE OF REVIEW: Seizures, including status epilepticus, have been reported in association with acute COVID-19 infection. People with epilepsy (PWE) have suffered from seizure exacerbations during the pandemic. This article reviews the data for clinical and electrographic seizures associated with COVID-19, technical EEG considerations for reducing risk of transmission, and factors contributing to seizure exacerbations in PWE as well as strategies to address this issue. RECENT FINDINGS: An increasing number of studies of larger cohorts, accounting for a variety of variables and often utilizing EEG with standardized terminology, are assessing the prevalence of seizures in hospitalized patients with acute COVID-19 infections, and gaining insight into the prevalence of seizures and their effect on outcomes. Additionally, recent studies are evaluating the effect of the pandemic on PWE, barriers faced, and the usefulness of telehealth. Although there is still much to learn regarding COVID-19, current studies help in assessing the risk of seizures, guiding EEG utilization, and optimizing the use of telehealth during the pandemic.

A Prospective Study of Neurologic Disorders in Hospitalized Patients With COVID-19 in New York City.

OBJECTIVE: To determine the prevalence and associated mortality of well-defined neurologic diagnoses among patients with coronavirus disease 2019 (COVID-19), we prospectively followed hospitalized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients and recorded new neurologic disorders and hospital outcomes. METHODS: We conducted a prospective, multicenter, observational study of consecutive hospitalized adults in the New York City metropolitan area with laboratory-confirmed SARS-CoV-2 infection. The prevalence of new neurologic disorders (as diagnosed by a neurologist) was recorded and in-hospital mortality and discharge disposition were compared between patients with COVID-19 with and without neurologic disorders. RESULTS: Of 4,491 patients with COVID-19 hospitalized during the study timeframe, 606 (13.5%) developed a new neurologic disorder in a median of 2 days from COVID-19 symptom onset. The most common diagnoses were toxic/metabolic encephalopathy (6.8%), seizure (1.6%), stroke (1.9%), and hypoxic/ischemic injury (1.4%). No patient had meningitis/encephalitis or myelopathy/myelitis referable to SARS-CoV-2 infection and 18/18 CSF specimens were reverse transcriptase PCR negative for SARS-CoV-2. Patients with neurologic disorders were more often older, male, white, hypertensive, diabetic, intubated, and had higher sequential organ failure assessment (SOFA) scores (all p < 0.05). After adjusting for age, sex, SOFA scores, intubation, history, medical complications, medications, and comfort care status, patients with COVID-19 with neurologic disorders had increased risk of in-hospital mortality (hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.17-1.62, p < 0.001) and decreased likelihood of discharge home (HR 0.72, 95% CI 0.63-0.85, p < 0.001). CONCLUSIONS: Neurologic disorders were detected in 13.5% of patients with COVID-19 and were associated with increased risk of in-hospital mortality and decreased likelihood of discharge home. Many observed neurologic disorders may be sequelae of severe systemic illness.

The Impact of Clinical Seizure Characteristics on Recognition and Treatment of New-onset Focal Epilepsy in Emergency Departments.

OBJECTIVE: Many people with new-onset focal epilepsy initially seek evaluation in emergency departments (EDs), and treatment decisions in EDs can influence likelihood of seizure recurrence. Using data collected for the Human Epilepsy Project (HEP), we assessed the effect of clinical seizure characteristics on ED clinical management. METHODS: There were 447 participants with new-onset focal epilepsy seen within 4 months of treatment initiation who were eligible and enrolled in HEP. Seizure calendars and medical records were collected. Based on clinical descriptions, seizures were categorized by semiology according to International League Against Epilepsy (ILAE) classifications as either focal nonmotor or focal motor seizures. RESULTS: Overall, 279 of 447(62%) of participants had presented to an ED prior to or at time of epilepsy diagnosis. A total of 132 of 246 (53%) with initial nonmotor seizures presented to an ED. Of these, eight (6%) presented with a first-lifetime nonmotor seizure. The other 124 (94%) presented after multiple seizures: seven (5%) with multiple nonmotor seizures and 117 (89%) with a first-lifetime motor seizure after having prior nonmotor seizures. A total of 147 of 201 (73%) participants with initial motor seizures presented to an ED. Of these, 134 (92%) presented with a first-lifetime motor seizure and 13 (9%) with multiple motor seizures. There was no difference in the likelihood of antiseizure medication initiation between participants who had multiple prior nonmotor seizures followed by a motor seizure (thereby fulfilling the criterion for an epilepsy diagnosis) versus those presenting with a single lifetime motor seizure (39% vs. 43%). There was no difference in recognition of seizures as the presenting complaint (85% vs. 87%) or whether the participant was admitted or referred to a neurologist (87% vs. 79%). CONCLUSIONS: This study contributes to evidence of underrecognition of nonmotor focal seizure semiologies in ED settings, which can support large-scale interventions aimed at improving recognition, specialist consultation, and treatment in ED settings.

Cross talk between drug-resistant epilepsy and the gut microbiome.

One-third of epilepsy patients have drug-resistant epilepsy (DRE), which is often complicated by polydrug toxicity and psychiatric and cognitive comorbidities. Advances in understanding the microbiome and gut-brain-axis are likely to shed light on epilepsy pathogenesis, anti-seizure medication (ASM) resistance, and potential therapeutic targets. Gut dysbiosis is associated with inflammation, blood-brain barrier disruption, and altered neuromodulators. High-throughput and metagenomic sequencing has advanced the characterization of microbial species and functional pathways. DRE patients show altered gut microbiome composition compared to drug-sensitive patients and healthy controls. The ketogenic and modified Atkins diets can reduce seizures in some patients with DRE. These low-carbohydrate dietary therapies alter the taxonomic and functional composition of the gut microbiome, and composition varies between diet responders and nonresponders. Murine models suggest that specific phyla are necessary to confer efficacy from the diet, and antibiotic treatment may eliminate efficacy. The impact of diet might involve alterations in microbiota, promotion of select microbial interactions, and variance in brain neurotransmitter levels that then influence seizures. Understanding the mechanics of how diet manipulates seizures may suggest novel therapies. Most ASMs act on neuronal transmission via effects on ion channels and neurotransmitters. However, ASMs may also assert their effects via the gut microbiota. In animal models, the microbiota composition (eg, abundance of certain phyla) can vary with ASM active drug metabolites. Given the developing understanding of the gut microbiome in DRE, probiotics are another potential therapy. Probiotics alter the microbiota composition, and small studies suggest that these supplements can reduce seizures in some patients. DRE has enormous consequences to patients and society, and the gut microbiome holds promise as a potential therapeutic target. However, the exact mechanism and recognition of which patients are likely to be responders remain elusive. Further studies are warranted.

Focal nonmotor versus motor seizures: The impact on diagnostic delay in focal epilepsy.

OBJECTIVE: To test the hypothesis that people with focal epilepsy experience diagnostic delays that may be associated with preventable morbidity, particularly when seizures have only nonmotor symptoms, we compared time to diagnosis, injuries, and motor vehicle accidents (MVAs) in people with focal nonmotor versus focal seizures with motor involvement at epilepsy onset. METHODS: This retrospective study analyzed the enrollment data from the Human Epilepsy Project, which enrolled participants between 2012 and 2017 across 34 sites in the USA, Canada, Europe, and Australia, within 4 months of treatment for focal epilepsy. A total of 447 participants were grouped by initial seizure semiology (focal nonmotor or focal with motor involvement) to compare time to diagnosis and prediagnostic injuries including MVAs. RESULTS: Demographic characteristics were similar between groups. There were 246 participants (55%) with nonmotor seizures and 201 participants (45%) with motor seizures at epilepsy onset. Median time to diagnosis from first seizure was 10 times longer in patients with nonmotor seizures compared to motor seizures at onset (P < .001). The number and severity of injuries were similar between groups. However, 82.6% of MVAs occurred in patients with undiagnosed nonmotor seizures. SIGNIFICANCE: This study identifies reasons for delayed diagnosis and consequences of delay in patients with new onset focal epilepsy, highlighting a treatment gap that is particularly significant in patients who experience nonmotor seizures at epilepsy onset.

Continuous EEG findings in patients with COVID-19 infection admitted to a New York academic hospital system.

OBJECTIVE: There is evidence for central nervous system complications of coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Encephalopathy caused by or arising from seizures, especially nonconvulsive seizures (NCS), often requires electroencephalography (EEG) monitoring for diagnosis. The prevalence of seizures and other EEG abnormalities among COVID-19-infected patients is unknown. METHODS: Medical records and EEG studies of patients hospitalized with confirmed COVID-19 infections over a 2-month period at a single US academic health system (four hospitals) were reviewed to describe the distribution of EEG findings including epileptiform abnormalities (seizures, periodic discharges, or nonperiodic epileptiform discharges). Factors including demographics, remote and acute brain injury, prior history of epilepsy, preceding seizures, critical illness severity scores, and interleukin 6 (IL-6) levels were compared to EEG findings to identify predictors of epileptiform EEG abnormalities. RESULTS: Of 111 patients monitored, most were male (71%), middle-aged or older (median age 64 years), admitted to an intensive care unit (ICU; 77%), and comatose (70%). Excluding 11 patients monitored after cardiac arrest, the most frequent EEG finding was moderate generalized slowing (57%), but epileptiform findings were observed in 30% and seizures in 7% (4% with NCS). Three patients with EEG seizures did not have epilepsy or evidence of acute or remote brain injury, although all had clinical seizures prior to EEG. Only having epilepsy (odds ratio [OR] 5.4, 95% confidence interval [CI] 1.4-21) or seizure(s) prior to EEG (OR 4.8, 95% CI 1.7-13) was independently associated with epileptiform EEG findings. SIGNIFICANCE: Our study supports growing evidence that COVID-19 can affect the central nervous system, although seizures are unlikely a common cause of encephalopathy. Seizures and epileptiform activity on EEG occurred infrequently, and having a history of epilepsy or seizure(s) prior to EEG testing was predictive of epileptiform findings. This has important implications for triaging EEG testing in this population.

Sounds of seizures.

PURPOSE: A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS: We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS: The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION: This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy.

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.

Early myoclonus following anoxic brain injury.

BACKGROUND: It is unknown whether postanoxic cortical and subcortical myoclonus are distinct entities with different prognoses. METHODS: In this retrospective cohort study of 604 adult survivors of cardiac arrest over 8.5 years, we identified 111 (18%) patients with myoclonus. Basic demographics and clinical characteristics of myoclonus were collected. EEG reports, and, when available, raw video EEG, were reviewed, and all findings adjudicated by 3 authors blinded to outcomes. Myoclonus was classified as cortical if there was a preceding, time-locked electrographic correlate and otherwise as subcortical. Outcome at discharge was determined using Cerebral Performance Category. RESULTS: Patients with myoclonus had longer arrests with less favorable characteristics compared to patients without myoclonus. Cortical myoclonus occurred twice as often as subcortical myoclonus (59% vs 23%, respectively). Clinical characteristics during hospitalization did not distinguish the two. Rates of electrographic seizures were higher in patients with cortical myoclonus (43%, vs 8% with subcortical). Survival to discharge was worse for patients with myoclonus compared to those without (26% vs 39%, respectively), but did not differ between subcortical and cortical myoclonus (24% and 26%, respectively). Patients with cortical myoclonus were more likely to be discharged in a comatose state than those with subcortical myoclonus (82% vs 33%, respectively). Among survivors, good functional outcome at discharge was equally possible between those with cortical and subcortical myoclonus (12% and 16%, respectively). CONCLUSIONS: Cortical and subcortical myoclonus are seen in every sixth patient with cardiac arrest and cannot be distinguished using clinical criteria. Either condition may have good functional outcomes.

A major miss in prognostication after cardiac arrest: Burst suppression and brain healing.

We report a case with therapeutic hypothermia after cardiac arrest where meaningful recovery far exceeded anticipated negative endpoints following cardiac arrest with loss of brainstem reflexes and subsequent status epilepticus. This man survived and recovered after an out-of-hospital cardiac arrest followed by a 6-week coma with absent motor responses and 5 weeks of burst suppression. Standard criteria suggested no chance of recovery. His recovery may relate to the effect of burst-suppression on EEG to rescue neurons near neuronal cell death. Further research to understand the mechanisms of therapeutic hypothermia and late restoration of neuronal functional capacity may improve prediction and aid end-of-life decisions after cardiac arrest.