Faculty Bibliography

Importance/UNASSIGNED:The true incidence of sudden unexplained death in childhood (SUDC), already the fifth leading category of death among toddlers by current US Centers for Disease Control and Prevention estimates, is potentially veiled by the varied certification processes by medicolegal investigative offices across the United States. Objective/UNASSIGNED:To evaluate the frequency of SUDC incidence, understand its epidemiology, and assess the consistency of death certification among medical examiner and coroner offices in the US death investigation system. Design, Setting, and Participants/UNASSIGNED:In this case series, 2 of 13 forensic pathologists (FPs) conducted masked reviews of 100 cases enrolled in the SUDC Registry and Research Collaborative (SUDCRRC). Children who died aged 11 months to 18 years from 36 US states, Canada, and the United Kingdom had been posthumously enrolled in the SUDCRRC by family members from 2014 to 2017. Comprehensive data from medicolegal investigative offices, clinical offices, and family members were reviewed. Data analysis was conducted from December 2014 to June 2020. Main Outcomes and Measures/UNASSIGNED:Certified cause of death (COD) characterized as explained (accidental or natural) or unexplained, as determined by SUDCRRC masked review process. Results/UNASSIGNED:In this study of 100 cases of SUDC (mean [SD] age, 32.1 [31.8] months; 58 [58.0%] boys; 82 [82.0%] White children; 92 [92.0%] from the United States), the original pathologist certified 43 cases (43.0%) as explained COD and 57 (57.0%) as unexplained COD. The SUDCRRC review process led to the following certifications: 16 (16.0%) were explained, 7 (7.0%) were undetermined because of insufficient data, and 77 (77.0%) were unexplained. Experts disagreed with the original COD in 40 cases (40.0%). These data suggest that SUDC incidence is higher than the current Centers for Disease Control and Prevention estimate (ie, 392 deaths in 2018). Conclusions and Relevance/UNASSIGNED:To our knowledge, this is the first comprehensive masked forensic pathology review process of sudden unexpected pediatric deaths, and it suggests that SUDC may often go unrecognized in US death investigations. Some unexpected pediatric deaths may be erroneously attributed to a natural or accidental COD, negatively affecting surveillance, research, public health funding, and medical care of surviving family members. To further address the challenges of accurate and consistent death certification in SUDC, future studies are warranted.

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/CONCLUSIONS: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.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

OBJECTIVE:To determine time trends and distinguishing autopsy findings of sudden unexpected death in epilepsy (SUDEP) in the U.S. METHODS:We identified the decedents where epilepsy/seizure was listed as cause/contributor to death, or comorbid condition on death certificate among all decedents who underwent medico-legal investigation at three medical examiner (ME) offices across the country: New York City (2009-2016), San Diego County (2008-2016), and Maryland (2000-2016). After reviewing all available reports, deaths classified as definite/probable/near SUDEP or SUDEP plus were included for analysis. Mann-Kendall trend test was used to analyze temporal trends in SUDEP rate for 2009-2016. Definite SUDEPs were compared to sex- and age ± 2 years-matched non-SUDEP deaths with a history of epilepsy regarding autopsy findings, circumstances, and comorbidities. RESULTS:1086 SUDEP cases were identified. There was a decreasing trend in ME-investigated SUDEP incidence between 2009-2016 (z= -2.2 S= -42 p= 0.028) among three regions. There was a 28% reduction in ME-investigated SUDEP incidence from 2009-2012 to 2013-2016 (CI: 17%-38%, p<0.0001). We found no correlation between SUDEP rates and the month of year or day of week. There was no difference between SUDEP and non-SUDEP deaths regarding neurodevelopmental abnormalities, pulmonary congestion/edema, and myocardial fibrosis. CONCLUSIONS:There was a decreasing monotonic trend in ME-investigated SUDEP incidence over eight years, with a 28% reduction in incidence from 2009-2012 to 2013-2016. Unlike SIDS and sudden cardiac death, we found no correlation between SUDEP and the season of year or day of week. No autopsy findings distinguished SUDEP from non-SUDEP deaths.

OBJECTIVE:To determine the impact of socioeconomic status (SES) on sudden unexpected death in epilepsy (SUDEP) rates. METHODS:We queried all decedents presented for medico-legal investigation at 3 medical examiner (ME) offices across the country (New York City, Maryland, San Diego County) in 2009 to 2010 and 2014 to 2015. We identified all decedents for whom epilepsy/seizure was listed as cause/contributor to death or comorbid condition on the death certificate. We then reviewed all available reports. Decedents determined to have SUDEP were included for analysis. We used median income in the ZIP code of residence as a surrogate for SES. For each region, zip code regions were ranked by median household income and divided into quartiles based on total population for 2 time periods. Region-, age-, and income-adjusted epilepsy prevalence was estimated in each zip code. SUDEP rates in the highest and lowest SES quartiles were evaluated to determine disparity. Examined SUDEP rates in 2 time periods were also compared. RESULTS:< 0.0001). CONCLUSION/CONCLUSIONS:ME-investigated SUDEP incidence was significantly higher in people with the lowest SES compared to the highest SES. The difference persisted over a 5-year period despite decreased overall SUDEP rates.

OBJECTIVE:To assess relative rates and clinical features of patients with genetic generalized epilepsy (GGE), focal epilepsy (FE), and developmental encephalopathic epilepsy (DEE) in the North American SUDEP Registry (NASR). METHODS:We identified all adjudicated definite, definite plus, and probable sudden unexpected death in epilepsy (SUDEP) cases (n = 262) and determined epilepsy type (GGE, FE, or DEE) from medical record review including history, imaging and EEG results, genetics, and next-of-kin interviews. RESULTS:Of the 262 SUDEP cases, 41 occurred in GGE, 95 in FE, 24 in DEE, and 102 were unclassifiable. GGE cases comprised 26% of NASR cases with an epilepsy syndrome diagnosis. The relative frequency of FE:GGE was slightly lower (2.3:1) than in population cohorts (2.1-6:1). Compared to patients with FE, patients with GGE had similar (1) ages at death and epilepsy onset and rates of (2) terminal and historical antiseizure medication adherence; (3) abnormal cardiac pathology; (4) illicit drug/alcohol use histories; and (5) sleep state when SUDEP occurred. CONCLUSIONS:GGE cases were relatively overrepresented in NASR. Because GGEs are less often treatment-resistant than FE or DEE, seizure type rather than frequency may be critical. Many people with GGE predominantly have generalized tonic-clonic seizures (GTCS) when they have uncontrolled or breakthrough seizures, whereas patients with FE more commonly experience milder seizures. Future mechanistic SUDEP studies should assess primary and focal-to-bilateral GTCS to identify potential differences in postictal autonomic and arousal disorders and to determine the differential role that lifestyle factors have on breakthrough seizures and seizure types in GGE vs FE to effectively target SUDEP mechanisms and prevention.

PURPOSE/OBJECTIVE: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/CONCLUSIONS: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.

Excess mortality due to epilepsy is greatest among young adults. However, the relative proportions of sudden unexpected death in epilepsy (SUDEP) and other epilepsy-related causes of death are not well defined. We prospectively adjudicated cause of death in all 18- to 45-year-olds with a history of seizure/epilepsy who underwent medicolegal investigation in San Diego County between 2014 and 2017. We identified 108 decedents with definite or probable epilepsy; 62% died from an epilepsy-related cause. SUDEP accounted for 42.6% (N = 46) of deaths, which were usually unwitnessed deaths, at home in bed. Other frequent causes of death were drug overdose (N = 23), suicide (N = 8), trauma (N = 8), and drowning (N = 6). SUDEP autopsies were similar to those of decedents from other causes. Most deaths in young adults with epilepsy that undergo medico-legal investigation are epilepsy-related, and SUDEP is the leading cause. Improved seizure control can potentially save many lives.

BACKGROUND:Early accounts of forced thought were reported at the onset of a focal seizure, and characterized as vague, repetitive, and involuntary intellectual auras distinct from perceptual or psychic hallucinations or illusions. Here, we examine the neural underpinnings involved in conceptual thought by presenting a series of 3 patients with epilepsy reporting intrusive thoughts during electrical stimulation of the left lateral prefrontal cortex (PFC) during invasive surgical evaluation. We illustrate the widespread networks involved through two independent brain imaging modalities: resting state functional magnetic resonance imaging (fMRI) (rs-fMRI) and task-based meta-analytic connectivity modeling (MACM). METHODS:We report the clinical and stimulation characteristics of three patients with left hemispheric language dominance who demonstrate forced thought with functional mapping. To examine the brain networks underlying this phenomenon, we used the regions of interest (ROI) centered at the active electrode pairs. We modeled functional networks using two approaches: (1) rs-fMRI functional connectivity analysis, representing 81 healthy controls and (2) meta-analytic connectivity modeling (MACM), representing 8260 healthy subjects. We also determined the overlapping regions between these three subjects' rs-fMRI and MACM networks through a conjunction analysis. RESULTS:We identified that left PFC was associated with a large-scale functional network including frontal, temporal, and parietal regions, a network that has been associated with multiple cognitive functions including semantics, speech, attention, working memory, and explicit memory. CONCLUSIONS:We illustrate the neural networks involved in conceptual thought through a unique patient population and argue that PFC supports this function through activation of a widespread network.