Faculty Bibliography

Presence of focal to bilateral tonic-clonic seizures (FBTCS) in focal epilepsy is associated with increased morbidity and mortality. Risk factors for FBTCS are poorly understood, and little is known regarding FBTCS recurrence after treatment initiation. This study aimed to investigate factors related to FBTCS in newly diagnosed focal epilepsy and their recurrence after starting antiseizure medications (ASMs) in the Human Epilepsy Project (HEP) cohort. HEP was an international, prospective cohort study that enrolled people with newly diagnosed focal epilepsy within 4 months of treatment initiation and followed them for up to 6 years. Baseline characteristics, treatment choices, and seizure outcomes were collected. Descriptive and inferential statistical analysis was conducted to assess the differences between study participants who had FBTCS and those who never experienced FBTCS. A total of 443 participants were included in this analysis; 77% (n = 342) had FBTCS at some point prior to or within the study period. In participants with FBTCS, regardless of initial seizure type, diagnosis was mostly made after FBTCS (335/342, 98%). After treatment initiation, FBTCS did not recur in 57% (n = 194/342) of cases. A higher number of total pretreatment seizures (median = 16 vs. 11, p = .048, Mann-Whitney U-test), predominantly focal aware seizures (FAS) or focal impaired awareness seizures (FIAS; median = 15 vs. 10, p = .049, Mann Whitney U-test), was associated with no recurrence in FBTCS after treatment initiation. Of 108 participants without FBTCS prior to treatment, only seven (6%) developed FBTCS after treatment initiation. There was no significant difference in choice of initial ASM class (levetiracetam vs. sodium channel blockers) between participants who experienced FBTCS and those who did not. This study highlights the significance of FBTCS among individuals with newly diagnosed focal epilepsy. The majority of participants who experienced FBTCS were diagnosed with epilepsy after experiencing their first FBTCS despite preceding FAS/FIAS. The more frequent FAS/FIAS in participants whose FBTCS resolved may be a characteristic of their epilepsy.

OBJECTIVE:Seizures can impact cognition both acutely and chronically. However, among those without significant comorbidities and broadly average cognition at epilepsy onset, the relationship between cognitive function at the time of diagnosis and long-term seizure control has been relatively unexplored. This analysis investigated associations between participant characteristics including specific aspects of cognitive performance at the time of focal epilepsy diagnosis and antiseizure medication (ASM) treatment resistance. METHODS:This was a secondary analysis of Human Epilepsy Project (HEP) data, which enrolled people with newly diagnosed focal epilepsy and broadly average cognition (estimated IQ ≥ 70) from June 29, 2012, to September 1, 2019. Participants analyzed in this study were between 18 and 60 years old, and scored within an acceptable range (i.e., Standard Score of ≥80) on measures estimating premorbid cognitive ability were offered the Cogstate Brief Battery (CBB). Participant characteristics were analyzed, including the presence of any anxiety disorders or depression, and summary CBB scores. HEP participants who were classified by the study as treatment resistant if they had experienced failure to achieve seizure freedom after two adequate trials of ASMs. Treatment resistance was modeled using multiple logistic regression to assess for independent associations between attention and working memory after correcting for the presence of the other potentially explanatory variables. RESULTS:200 HEP participants had comprehensive enrollment records including CBB results and complete seizure outcome data for analysis in this study. After correcting for potentially confounding variables, there were no independent associations between cognitive measures on the CBB at the time of enrollment and subsequent development of ASM treatment resistance. Specifically, z-scores for reaction time on the CBB (an average of the CBB Identification and Detection tests) were not associated with treatment resistance (p = 0.51) and z-scores for memory performance (an average of the CBB One Card Learning test and One Back tests) were not associated with treatment resistance (p = 0.24). There were no significant independent associations between age or the presence of depression or anxiety disorders at the time of CBB testing and treatment resistance. However, there was an independent association between employment status and treatment resistance, with those who were employed or students (>18 years old) at the time of enrollment and CBB testing having 0.35 times lower odds of treatment resistance (95 %CI 0.15-0.81, p = 0.01). SIGNIFICANCE/CONCLUSIONS:The findings from this study suggest that in otherwise healthy people with new onset focal epilepsy who have broadly average intelligence, attention and working memory as measured by the CBB at the time of diagnosis is not associated with treatment resistance. Although performance on cognitive testing at epilepsy onset may not be predictive of risk of treatment resistance in this population, other individual characteristics such as employment status at the time of diagnosis may be indirect markers of long-term seizure outcomes and require further investigation.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Systematic screening can help identify antiseizure medication (ASM)-associated adverse events (AEs) that may preclude patients from reaching effective doses or completing adequate trial periods. The Adverse Event Profile (AEP) is a self-completed instrument to identify the frequency of common AEs associated with ASM use. This study aimed to compare the AE profile of commonly used ASMs in adults with newly diagnosed focal epilepsy. METHODS:The Human Epilepsy Project is a prospective, international, observational study investigating markers of treatment response in newly diagnosed focal epilepsy. Participants were enrolled within 4 months of treatment initiation. Adult participants on levetiracetam, lamotrigine, carbamazepine, or oxcarbazepine monotherapy who completed the AEP and Mini International Neuropsychiatric Interview at enrollment were included. Multivariable generalized linear and penalized logistic regression models assessed differences in total and itemized marginal AEP scores and dichotomized responses ("never/rarely" vs "sometimes/always"). RESULTS:= 0.047) than lamotrigine users. Carbamazepine and oxcarbazepine had the highest rates of discontinuation (42.1%, each), followed by levetiracetam (34.8%) and lamotrigine (16.4%). Levetiracetam users had the highest proportion of discontinuations because of AEs alone (18%), and lamotrigine had the lowest (5%). DISCUSSION/CONCLUSIONS:Systematic screening for AEs in adults with newly diagnosed focal epilepsy on ASM monotherapy showed that those with comorbid psychiatric conditions report greater AEs overall, irrespective of ASM. Levetiracetam was associated with >3-fold risk of psychiatric AEs and half the risk of experiencing unsteadiness than lamotrigine. Levetiracetam had the highest proportion of discontinuations because of AEs alone, while lamotrigine had the lowest.

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/CONCLUSIONS: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.

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: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: DISCUSSION: CLASSIFICATION OF EVIDENCE:can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

BACKGROUND AND OBJECTIVES/OBJECTIVE: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). Prior inter-rater 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 (LPD)", "Generalized Periodic Discharges (GPD)", "Lateralized Rhythmic Delta Activity (LRDA)", "Generalized Rhythmic Delta Activity (GRDA)", or "Other". EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 to 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:: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise, but rather to variation in decision thresholds. DISCUSSION/CONCLUSIONS: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/METHODS:This study provides Class II evidence that independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared to expert consensus.

BACKGROUND AND OBJECTIVES/OBJECTIVE: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: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/CONCLUSIONS: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.

PURPOSE/OBJECTIVE: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.

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 four 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 non-motor or focal motor seizures. RESULTS:Overall, 279/447(62%) of participants had presented to an ED prior to or at time of epilepsy diagnosis. 132 /246 (53%) with initial non-motor seizures presented to an ED. Of these, 8 (6%) presented with a first-lifetime non-motor seizure. The other 124 (94%) presented after multiple seizures: 7 (5%) with multiple non-motor seizures, and 117 (89%) with a first-lifetime motor seizure after having prior non-motor seizures. 147/ 201 (73%) of 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 anti-seizure medication (ASM) initiation between participants who had multiple prior non-motor seizures followed by a motor seizure (thereby fulfilling the criterion for an epilepsy diagnosis), vs 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 under-recognition of non-motor focal seizure semiologies in ED settings, which can support large-scale interventions aimed at improving recognition, specialist consultation, and treatment in ED settings.

OBJECTIVE:To determine the prevalence and associated mortality of well-defined neurologic diagnoses among COVID-19 patients, we prospectively followed hospitalized SARS-Cov-2 positive patients and recorded new neurologic disorders and hospital outcomes. METHODS:We conducted a prospective, multi-center, observational study of consecutive hospitalized adults in the NYC 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 COVID-19 patients with and without neurologic disorders. RESULTS:Of 4,491 COVID-19 patients 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 RT-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, past history, medical complications, medications and comfort-care-status, COVID-19 patients with neurologic disorders had increased risk of in-hospital mortality (Hazard Ratio[HR] 1.38, 95% 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 COVID-19 patients 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.