The complexities underlying epilepsy in people with glioblastoma.

Seizures are among the most common clinical signs in people with glioblastoma. Advances over the past 5 years, including new clinical trial data, have increased the understanding of why some individuals with glioblastoma are susceptible to seizures, how seizures manifest clinically, and what implications seizures have for patient management. The pathophysiology of epilepsy in people with glioblastoma relates to a combination of intrinsic epileptogenicity of tumour tissue, alterations in the tumour and peritumoural microenvironment, and the physical and functional disturbance of adjacent brain structures. Successful management of epilepsy in people with glioblastoma remains challenging; factors such as drug-drug interactions between cancer therapies and antiseizure medications, and medication side-effects, can affect seizure outcomes and quality of life. Advances in novel therapies provide some promise for people with glioblastoma; however, the effects of these therapies on seizures are yet to be fully determined. Looking forward, insights into electrical activity as a driver of tumour cell growth and the intrinsic hyperexcitability of tumour tissue might represent useful targets for treatment and disease modification. There is a pressing need for large randomised clinical trials in this field.

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.

Experimental Neurotherapeutics: Surfing the Tidal Wave of New Opportunities.

The subspecialty of experimental neurotherapeutics trains neurologists in discovering and developing new treatments for neurologic diseases. Based on development of exciting new treatments for genetic and inflammatory diseases, we predict that there will be many other breakthroughs. The job market has expanded rapidly in academia, the pharmaceutical industry, government, and not-for-profit sectors; many new opportunities can be anticipated. The burgeoning opportunities in the field mandate that training address the challenges of overcoming obstacles in therapeutic discovery, implementation science, and development of affordable and equitably available treatments. ANN NEUROL 2023;93:427-430.

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.

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.

EEG reading with or without clinical information - a real-world practice study.

We sought to investigate electroencephalographers' real-world behaviors and opinions concerning reading routine EEG (rEEG) with or without clinical information. An eight-question, anonymous, online survey targeted at electroencephalographers was disseminated on social media from the authors' personal accounts and emailed to authors' select colleagues. A total of 389 responses were included. Most respondents reported examining clinical information before describing rEEG findings. Nonetheless, only a minority of respondents believe that EEG analysis/description should be influenced by clinical information. We recommend reviewing clinical data only after an unbiased EEG read to prevent history bias and ensure generation of reliable electrodiagnostic information.

Noninvasive Detection of Hippocampal Epileptiform Activity on Scalp Electroencephalogram.

Importance: The hippocampus is a highly epileptogenic brain region, yet over 90% of hippocampal epileptiform activity (HEA) cannot be identified on scalp electroencephalogram (EEG) by human experts. Currently, detection of HEA requires intracranial electrodes, which limits our understanding of the role of HEA in brain diseases. Objective: To develop and validate a machine learning algorithm that accurately detects HEA from a standard scalp EEG, without the need for intracranial electrodes. Design, Setting, and Participants: In this diagnostic study, conducted from 2008 to 2021, EEG data were used from patients with temporal lobe epilepsy (TLE) and healthy controls (HCs) to train and validate a deep neural network, HEAnet, to detect HEA on scalp EEG. Participants were evaluated at tertiary-level epilepsy centers at 2 academic hospitals: Massachusetts General Hospital (MGH) or Brigham and Women's Hospital (BWH). Included in the study were patients aged 12 to 78 years with a clinical diagnosis of TLE and HCs without epilepsy. Patients with TLE and HCs with a history of intracranial surgery were excluded from the study. Exposures: Simultaneous intracranial EEG and/or scalp EEG. Main Outcomes and Measures: Performance was assessed using cross-validated areas under the receiver operating characteristic curve (AUC ROC) and precision-recall curve (AUC PR) and additional clinically relevant metrics. Results: HEAnet was trained and validated using data sets that were derived from a convenience sample of 141 eligible participants (97 with TLE and 44 HCs without epilepsy) whose retrospective EEG data were readily available. Data set 1 included the simultaneous scalp EEG and intracranial electrode recordings of 51 patients with TLE (mean [SD] age, 40.7 [15.9] years; 30 men [59%]) at MGH. An automatically generated training data set with 972 095 positive HEA examples was created, in addition to a held-out expert-annotated testing data set with 22 762 positive HEA examples. HEAnet's performance was validated on 2 independent scalp EEG data sets: (1) data set 2 (at MGH; 24 patients with TLE and 20 HCs; mean [SD] age, 42.3 [16.2] years; 17 men [39%]) and (2) data set 3 (at BWH; 22 patients with TLE and 24 HCs; mean [SD] age, 43.0 [14.4] years; 20 men [43%]). For single-event detection of HEA on data set 1, HEAnet achieved a mean (SD) AUC ROC of 0.89 (0.01) and a mean (SD) AUC PR of 0.39 (0.03). On external validation with data sets 2 and 3, HEAnet accurately distinguished TLE from HC (AUC ROC of 0.88 and 0.95, respectively) and predicted epilepsy lateralization with 100% and 92% accuracy, respectively. HEAnet tracked dynamic changes in HEA in response to seizure medication adjustments and performed comparably with human experts in diagnosing TLE from 1-hour scalp EEG recordings, diagnosing TLE in several individuals that experts missed. Without reducing specificity, addition of HEAnet to human expert EEG review increased sensitivity for diagnosing TLE in humans from 50% to 58% to 63% to 67%. Conclusions and Relevance: Results of this diagnostic study suggest that HEAnet provides a novel, noninvasive, quantitative, and clinically relevant biomarker of hippocampal hyperexcitability in humans.

Aura Type and Outcome After Anterior Temporal Lobectomy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is one of the most common causes of medically refractory focal epilepsy. Anterior temporal lobectomy (ATL) leads to improved seizure control in patients with medically refractory TLE. Various auras are associated with TLE; however, the relationships between aura type and outcome after ATL are poorly understood. Our objective was to investigate the associations among clinical features, aura type, and seizure outcome after ATL. METHODS: The records of patients who underwent ATL between 1993 and 2016 at a single institution (N = 174) were retrospectively reviewed. Demographic and clinical variables were compared among aura types using analysis of variance and logistic regression analysis. A multiple regression analysis was conducted to determine whether aura type predicted seizure outcome after ATL. RESULTS: Mesial temporal sclerosis (MTS) on magnetic resonance imaging inversely correlated with cephalic auras (P = 0.0090). Affective auras (P = 0.014) and somatosensory auras (P = 0.021) were correlated with findings of MTS on pathology, whereas this finding was inversely correlated with the presence of auditory auras (P = 0.0056). On multiple regression analysis, predictors of worse seizure outcome after ATL were cephalic auras (P = 0.0048), gustatory auras (P = 0.029), visual auras (P = 0.049), and tonic-clonic seizures (P = 0.047). Fewer preoperative antiepileptic medications (P = 0.0032), and presence of multiple auras (P = 0.011) were associated with better outcome. CONCLUSIONS: Cephalic auras, gustatory auras, and visual auras were associated with worse seizure outcome after ATL.

Safety and Efficacy of Natalizumab as Adjunctive Therapy for People With Drug-Resistant Epilepsy: A Phase 2 Study.

BACKGROUND AND OBJECTIVES: To explore efficacy/safety of natalizumab, a humanized monoclonal anti-α4-integrin antibody, as adjunctive therapy in adults with drug-resistant focal epilepsy. METHODS: Participants with ≥6 seizures during the 6-week baseline period were randomized 1:1 to receive natalizumab 300 mg IV or placebo every 4 weeks for 24 weeks. Primary efficacy outcome was change from baseline in log-transformed seizure frequency, with a predefined threshold for therapeutic success of 31% relative reduction in seizure frequency over the placebo group. Countable seizure types were focal aware with motor signs, focal impaired awareness, and focal to bilateral tonic-clonic. Secondary efficacy endpoints/safety were also assessed. RESULTS: Of 32 and 34 participants dosed in the natalizumab 300 mg and placebo groups, 30 (94%) and 31 (91%) completed the placebo-controlled treatment period, respectively (one participant was randomized to receive natalizumab but not dosed due to IV complications). Estimated relative change in seizure frequency of natalizumab over placebo was -14.4% (95% confidence interval [CI] -46.1%-36.1%; p = 0.51). The proportion of participants with ≥50% reduction from baseline in seizure frequency was 31.3% for natalizumab and 17.6% for placebo (odds ratio 2.09, 95% CI 0.64-6.85; p = 0.22). Adverse events were reported in 24 (75%) and 22 (65%) participants receiving natalizumab vs placebo. DISCUSSION: Although the threshold to demonstrate efficacy was not met, there were no unexpected safety findings and further exploration of possible anti-inflammatory therapies for drug-resistant epilepsy is warranted. TRIAL REGISTRATION INFORMATION: The ClinicalTrials.gov registration number is NCT03283371. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that IV natalizumab every 4 weeks, compared to placebo, did not significantly change seizure frequency in adults with drug-resistant epilepsy. The study lacked the precision to exclude an important effect of natalizumab.

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients. METHODS: A single center retrospective analysis was made of 1,967 neurologic, medical, and surgical patients who underwent >16 hours of continuous electroencephalography (EEG) between 2011 and 2017. We developed an artificial intelligence algorithm to annotate 11.02 terabytes of EEG and quantify epileptiform activity burden within 72 hours of recording. We evaluated burden (1) in the first 24 hours of recording, (2) in the 12-hours epoch with highest burden (peak burden), and (3) cumulatively through the first 72 hours of monitoring. Machine learning was applied to estimate the effect of epileptiform burden on outcome. Outcome measure was discharge modified Rankin Scale, dichotomized as good (0-4) versus poor (5-6). RESULTS: Peak epileptiform burden was independently associated with poor outcomes (p < 0.0001). Other independent associations included age, Acute Physiology and Chronic Health Evaluation II score, seizure on presentation, and diagnosis of hypoxic-ischemic encephalopathy. Model calibration error was calculated across 3 strata based on the time interval between last EEG measurement (up to 72 hours of monitoring) and discharge: (1) <5 days between last measurement and discharge, 0.0941 (95% confidence interval [CI] = 0.0706-0.1191); 5 to 10 days between last measurement and discharge, 0.0946 (95% CI = 0.0631-0.1290); >10 days between last measurement and discharge, 0.0998 (95% CI = 0.0698-0.1335). After adjusting for covariates, increase in peak epileptiform activity burden from 0 to 100% increased the probability of poor outcome by 35%. INTERPRETATION: Automated measurement of peak epileptiform activity burden affords a convenient, consistent, and quantifiable target for future multicenter randomized trials investigating whether suppressing epileptiform activity improves outcomes. ANN NEUROL 2021;90:300-311.

Responsive neurostimulation for focal motor status epilepticus.

No clear evidence-based treatment paradigm currently exists for refractory and super-refractory status epilepticus, which can result in significant mortality and morbidity. While patients are typically treated with antiepileptic drugs and anesthetics, neurosurgical neuromodulation techniques can also be considered. We present a novel case in which responsive neurostimulation was used to effectively treat a patient who had developed super-refractory status epilepticus, later consistent with epilepsia partialis continua, that was refractory to antiepileptic drugs, immunomodulatory therapies, and transcranial magnetic stimulation. This case demonstrates how regional therapy provided by responsive neurostimulation can be effective in treating super-refractory status epilepticus through neuromodulation of seizure networks.

Expert Perspective: Who May Benefit Most From the New Ultra Long-Term Subcutaneous EEG Monitoring?

Today's modalities for short-term monitoring of EEG are primarily meant for supporting clinical diagnosis of epilepsy or classifying seizures and interictal epileptiform discharges while long-term EEG adds the value of differential diagnosis investigation or pre-surgical evaluation. However, longitudinal epilepsy care relies on patient diaries, which is known to be unreliable for most patients and especially those with focal impaired awareness or nocturnal seizures. The subcutaneous ultra long-term EEG (ULT-EEG) systems alleviate those issue by enabling objective, continuous EEG monitoring for days, weeks, months, or years. Albeit a great advance in continuous EEG over extended periods, it comes with the caveat of limited spatial resolution of two channels. Therefore, the new subcutaneous EEG modality may be especially suited for a selected group of patients. We convened a panel of experienced epileptologists to consider the utility of a subcutaneous, two-channel ULT-EEG device with the goal of developing a consensus-based expert recommendation on selecting the optimal patient types for this investigative technique. The ideal patients to select for this type of monitoring would have focal impaired awareness seizures without predominant motor features and seizures with medium to high voltage patterns. As this technology matures and we learn more about its limitations and benefits we might find a wider array of use case scenarios as it is believed that the benefits for many patients are most likely to outweigh the risks and cost.

Association of epileptiform abnormalities and seizures in Alzheimer disease.

OBJECTIVE: To examine the relationship between scalp EEG biomarkers of hyperexcitability in Alzheimer disease (AD) and to determine how these electric biomarkers relate to the clinical expression of seizures in AD. METHODS: In this cross-sectional study, we performed 24-hour ambulatory scalp EEGs on 43 cognitively normal elderly healthy controls (HC), 41 participants with early-stage AD with no history or risk factors for epilepsy (AD-NoEp), and 15 participants with early-stage AD with late-onset epilepsy related to AD (AD-Ep). Two epileptologists blinded to diagnosis visually reviewed all EEGs and annotated all potential epileptiform abnormalities. A panel of 9 epileptologists blinded to diagnosis was then surveyed to generate a consensus interpretation of epileptiform abnormalities in each EEG. RESULTS: Epileptiform abnormalities were seen in 53% of AD-Ep, 22% of AD-NoEp, and 4.7% of HC. Specific features of epileptiform discharges, including high frequency, robust morphology, right temporal location, and occurrence during wakefulness and REM, were associated with clinical seizures in AD. Multiple EEG biomarkers concordantly demonstrated a pattern of left temporal lobe hyperexcitability in early stages of AD, whereas clinical seizures in AD were often associated with bitemporal hyperexcitability. Frequent small sharp spikes were specifically associated with epileptiform EEGs and thus identified as a potential biomarker of hyperexcitability in AD. CONCLUSION: Epileptiform abnormalities are common in AD but not all equivalent. Specific features of epileptiform discharges are associated with clinical seizures in AD. Given the difficulty recognizing clinical seizures in AD, these EEG features could provide guidance on which patients with AD are at high risk for clinical seizures.

Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy.

OBJECTIVE: To prospectively evaluate safety and efficacy of brain-responsive neurostimulation in adults with medically intractable focal onset seizures (FOS) over 9 years. METHODS: Adults treated with brain-responsive neurostimulation in 2-year feasibility or randomized controlled trials were enrolled in a long-term prospective open label trial (LTT) to assess safety, efficacy, and quality of life (QOL) over an additional 7 years. Safety was assessed as adverse events (AEs), efficacy as median percent change in seizure frequency and responder rate, and QOL with the Quality of Life in Epilepsy (QOLIE-89) inventory. RESULTS: Of 256 patients treated in the initial trials, 230 participated in the LTT. At 9 years, the median percent reduction in seizure frequency was 75% (p < 0.0001, Wilcoxon signed rank), responder rate was 73%, and 35% had a ≥90% reduction in seizure frequency. We found that 18.4% (47 of 256) experienced ≥1 year of seizure freedom, with 62% (29 of 47) seizure-free at the last follow-up and an average seizure-free period of 3.2 years (range 1.04-9.6 years). Overall QOL and epilepsy-targeted and cognitive domains of QOLIE-89 remained significantly improved (p < 0.05). There were no serious AEs related to stimulation, and the sudden unexplained death in epilepsy (SUDEP) rate was significantly lower than predefined comparators (p < 0.05, 1-tailed χ2). CONCLUSIONS: Adjunctive brain-responsive neurostimulation provides significant and sustained reductions in the frequency of FOS with improved QOL. Stimulation was well tolerated; implantation-related AEs were typical of other neurostimulation devices; and SUDEP rates were low. CLINICALTRIALSGOV IDENTIFIER: NCT00572195. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that brain-responsive neurostimulation significantly reduces focal seizures with acceptable safety over 9 years.

Burden of Epileptiform Activity Predicts Discharge Neurologic Outcomes in Severe Acute Ischemic Stroke.

BACKGROUND/OBJECTIVES: Clinical seizures following acute ischemic stroke (AIS) appear to contribute to worse neurologic outcomes. However, the effect of electrographic epileptiform abnormalities (EAs) more broadly is less clear. Here, we evaluate the impact of EAs, including electrographic seizures and periodic and rhythmic patterns, on outcomes in patients with AIS. METHODS: This is a retrospective study of all patients with AIS aged ≥ 18 years who underwent at least 18 h of continuous electroencephalogram (EEG) monitoring at a single center between 2012 and 2017. EAs were classified according to American Clinical Neurophysiology Society (ACNS) nomenclature and included seizures and periodic and rhythmic patterns. EA burden for each 24-h epoch was defined using the following cutoffs: EA presence, maximum daily burden < 10% versus > 10%, maximum daily burden < 50% versus > 50%, and maximum daily burden using categories from ACNS nomenclature ("rare" < 1%; "occasional" 1-9%; "frequent" 10-49%; "abundant" 50-89%; "continuous" > 90%). Maximum EA frequency for each epoch was dichotomized into ≥ 1.5 Hz versus < 1.5 Hz. Poor neurologic outcome was defined as a modified Rankin Scale score of 4-6 (vs. 0-3 as good outcome) at hospital discharge. RESULTS: One hundred and forty-three patients met study inclusion criteria. Sixty-seven patients (46.9%) had EAs. One hundred and twenty-four patients (86.7%) had poor outcome. On univariate analysis, the presence of EAs (OR 3.87 [1.27-11.71], p = 0.024) and maximum daily burden > 10% (OR 12.34 [2.34-210], p = 0.001) and > 50% (OR 8.26 [1.34-122], p = 0.035) were associated with worse outcomes. On multivariate analysis, after adjusting for clinical covariates (age, gender, NIHSS, APACHE II, stroke location, stroke treatment, hemorrhagic transformation, Charlson comorbidity index, history of epilepsy), EA presence (OR 5.78 [1.36-24.56], p = 0.017), maximum daily burden > 10% (OR 23.69 [2.43-230.7], p = 0.006), and maximum daily burden > 50% (OR 9.34 [1.01-86.72], p = 0.049) were associated with worse outcomes. After adjusting for covariates, we also found a dose-dependent association between increasing EA burden and increasing probability of poor outcomes (OR 1.89 [1.18-3.03] p = 0.009). We did not find an independent association between EA frequency and outcomes (OR: 4.43 [.98-20.03] p = 0.053). However, the combined effect of increasing EA burden and frequency ≥ 1.5 Hz (EA burden * frequency) was significantly associated with worse outcomes (OR 1.64 [1.03-2.63] p = 0.039). CONCLUSIONS: Electrographic seizures and periodic and rhythmic patterns in patients with AIS are associated with worse outcomes in a dose-dependent manner. Future studies are needed to assess whether treatment of this EEG activity can improve outcomes.

Burst Suppression: Causes and Effects on Mortality in Critical Illness.

BACKGROUND: Burst suppression in mechanically ventilated intensive care unit (ICU) patients is associated with increased mortality. However, the relative contributions of propofol use and critical illness itself to burst suppression; of burst suppression, propofol, and critical illness to mortality; and whether preventing burst suppression might reduce mortality, have not been quantified. METHODS: The dataset contains 471 adults from seven ICUs, after excluding anoxic encephalopathy due to cardiac arrest or intentional burst suppression for therapeutic reasons. We used multiple prediction and causal inference methods to estimate the effects connecting burst suppression, propofol, critical illness, and in-hospital mortality in an observational retrospective study. We also estimated the effects mediated by burst suppression. Sensitivity analysis was used to assess for unmeasured confounding. RESULTS: The expected outcomes in a "counterfactual" randomized controlled trial (cRCT) that assigned patients to mild versus severe illness are expected to show a difference in burst suppression burden of 39%, 95% CI [8-66]%, and in mortality of 35% [29-41]%. Assigning patients to maximal (100%) burst suppression burden is expected to increase mortality by 12% [7-17]% compared to 0% burden. Burst suppression mediates 10% [2-21]% of the effect of critical illness on mortality. A high cumulative propofol dose (1316 mg/kg) is expected to increase burst suppression burden by 6% [0.8-12]% compared to a low dose (284 mg/kg). Propofol exposure has no significant direct effect on mortality; its effect is entirely mediated through burst suppression. CONCLUSIONS: Our analysis clarifies how important factors contribute to mortality in ICU patients. Burst suppression appears to contribute to mortality but is primarily an effect of critical illness rather than iatrogenic use of propofol.

Contralateral Preoperative Resting-State Functional MRI Network Integration Is Associated with Surgical Outcome in Temporal Lobe Epilepsy.

Background Although most patients with medically refractory temporal lobe epilepsy (TLE) experience seizure freedom after anterior temporal lobectomy, approximately 40% may continue to have seizures. Functional network integration, as measured with preoperative resting-state functional MRI, may help stratify patients who are more likely to experience postoperative seizure freedom. Purpose To relate preoperative resting-state functional MRI and surgical outcome in patients with medically refractory TLE. Materials and Methods Data from patients with medically intractable TLE were retrospectively analyzed. Patients underwent preoperative resting-state functional MRI between March 2010 and April 2013 and subsequent unilateral anterior temporal lobectomy. Postoperative seizure-free status was categorized using the Engel Epilepsy Surgery Outcome Scale. Global and regional resting-state functional MRI network properties on preoperative functional MRI scans related to integration were calculated and statistically compared between patients who experienced complete postoperative seizure freedom (Engel class IA) and all others (Engel class IB to class IV) using t tests and multiple logistic regression. Results Forty patients (mean age, 34 years ± 15 [standard deviation]; 21 female) were evaluated. Preoperative global network integration was different (P = .01) between patients who experienced seizure freedom after surgery and all other patients, with 9% lower leaf fraction and 10% lower tree hierarchy in patients with ongoing seizures. Preoperative regional network integration in the contralateral temporoinsular region was different (P = .04) between patients in these two groups. Specifically, the group-level leaf proportion was 59% lower in the entorhinal cortex, 73% lower in the inferior temporal gyrus, 43% lower in the temporal pole, and 69% lower in the insula in patients with ongoing seizures after surgery. When using multivariate regression, contralateral temporoinsular leaf proportion (P = .002) and epilepsy duration (P = .04) were predictive of postoperative seizure freedom, while age (P > .70) and age at seizure onset (P > .50) were not. Conclusion Lower network integration globally and involving the contralateral temporoinsular cortex on preoperative resting-state functional MRI scans is associated with ongoing postoperative seizures in patients with temporal lobe epilepsy. © RSNA, 2020.