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Background and Objectives: The aim of this work was to test the accuracy of Persyst commercially available automated seizure detection in critical care EEG by comparing automated seizure detections to human review in a manually reviewed cohort and on a large scale., Methods: Automated seizure detections (Persyst versions 12 and 13) were compared to human review in a pilot cohort of 229 seizures from 85 EEG records and then in an expanded cohort of 7,924 EEG records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for individual seizures (pilot cohort) and for entire records (pilot and expanded cohorts). We assessed EEG features associated with the accuracy of automated seizure detections., Results: In the pilot cohort, accuracy of automated detection for individual seizures was modest (sensitivity 0.50, PPV 0.60). At the record level (did the recording contain seizures or not?), sensitivity was higher (pilot cohort 0.78, expanded cohort 0.91), PPV was low (pilot cohort 0.40, expanded cohort 0.08), and NPV was high (pilot cohort 0.88, expanded cohort 0.97). Different software versions (version 12 vs 13) performed similarly. Sensitivity was higher for records containing focal-onset seizures compared to generalized-onset seizures (0.93 vs 0.85, p = 0.012)., Discussion: In critical care continuous EEG recordings, automated detection of individual seizures had rates of both false negatives and false positives that bring into question its utility as a seizure alarm in clinical practice. At the level of entire EEG records, the absence of automated detections accurately predicted EEG records without true seizures. The true value of Persyst automated seizure detection appears to lie in triaging of low-risk EEGs., Classification of Evidence: This study provides Class II evidence that an automated seizure detection program cannot accurately identify EEG records that contain seizures., (© 2022 American Academy of Neurology.)

Objective: To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest., Methods: We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples., Results: The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes., Conclusions: QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest., (© 2019 American Academy of Neurology.)

Objective: To determine the 30-day readmission rate after seizure-related discharge in a nationally representative sample, as well as patient, clinical, and hospital characteristics associated with readmission., Methods: Retrospective cohort study of adults discharged alive from a nonelective hospitalization for epilepsy or seizure, sampled from the Healthcare Cost and Utilization Project's 2014 Nationwide Readmissions Database. Descriptive statistics and logistic regression models were built to quantify and characterize nonelective readmission within 30 days., Results: A total of 139,800 admissions met inclusion criteria, of which 15,094 (10.8%) were readmitted within 30 days. Patient characteristics associated with readmission included comorbid disease burden (Elixhauser score 2: adjusted odds ratio [AOR] [95% confidence interval (CI)] 1.38 [1.21-1.57]; Elixhauser score 3: AOR 1.52 [1.34-1.73]; Elixhauser score >4: AOR 2.28 [2.01-2.58] as compared to 1) and participation in public insurance programs (Medicare: AOR 1.39 [1.26-1.54]; Medicaid: AOR 1.39 [1.26-1.54] as compared to private insurance). Adverse events (AOR 1.17 [1.05-1.30]) and prolonged length of stay, as well as nonroutine discharge (AOR 1.32 [1.23-1.42]), were also associated with increased adjusted odds of readmission. The most common primary reason for readmission was epilepsy or convulsion (17%)., Conclusions: Patients hospitalized with seizure are frequently readmitted. While readmitted patients are more likely to have multiple medical comorbidities, our study demonstrated that inpatient adverse events were also significantly associated with readmission. The most common reason for readmission was seizure or epilepsy. Together, these 2 findings suggest that a proportion of readmissions are related to modifiable care process factors and may therefore be avoidable. Further study into understanding preventable drivers of readmission in this population presents an opportunity to improve patient outcomes and health., (© 2018 American Academy of Neurology.)

Objective: To characterize continuous EEG (cEEG) use patterns in the critically ill and to determine the association with hospitalization outcomes for specific diagnoses., Methods: We performed a retrospective cross-sectional study with National Inpatient Sample data from 2004 to 2013. We sampled hospitalized adult patients who received intensive care and then compared patients who underwent cEEG to those who did not. We considered diagnostic subgroups of seizure/status epilepticus, subarachnoid or intracerebral hemorrhage, and altered consciousness. Outcomes were in-hospital mortality, hospitalization cost, and length of stay., Results: In total, 7,102,399 critically ill patients were identified, of whom 22,728 received cEEG. From 2004 to 2013, the proportion of patients who received cEEG increased from 0.06% (95% confidence interval [CI] 0.03%-0.09%) to 0.80% (95% CI 0.62%-0.98%). While the cEEG cohort appeared more ill, cEEG use was associated with reduced in-hospital mortality after adjustment for patient and hospital characteristics (odds ratio [OR] 0.83, 95% CI 0.75-0.93, p < 0.001). This finding held for the diagnoses of subarachnoid or intracerebral hemorrhage and for altered consciousness but not for the seizure/status epilepticus subgroup. Cost and length of hospitalization were increased for the cEEG cohort (OR 1.17 and OR 1.11, respectively, p < 0.001)., Conclusions: There was a >10-fold increase in cEEG use from 2004 to 2013. However, this procedure may still be underused; cEEG was associated with lower in-hospital mortality but used for only 0.3% of the critically ill population. While administrative claims analysis supports the utility of cEEG for critically ill patients, our findings suggest variable benefit by diagnosis, and investigation with greater clinical detail is warranted., (© 2018 American Academy of Neurology.)

Background: This study investigated the quality of care delivered by nurse practitioner (NP)-physician teams employed to expand clinic appointment availability for patients with epilepsy., Methods: We performed a retrospective observational cohort study of patients with epilepsy presenting to the Penn Epilepsy Center for a new patient appointment in 2014. During this time, patients were seen either by an NP-physician team care model or a more traditional physician-only care model. These care models were compared with regard to adherence to the 2014 American Academy of Neurology epilepsy quality measures at the initial visit. Clinical outcomes of seizure frequency, presentations to the Emergency Department, injury, and death were assessed over the subsequent year., Results: A total of 169 patients were identified by our inclusion and exclusion criteria: 65 patients in the NP-physician team care model cohort and 104 patients in the physician-only care model cohort. The NP-physician team care model saw, on average, 3 more patients per clinic session. There were no meaningful differences between these cohorts in baseline characteristics. The NP-physician team care model showed equivalent adherence to the physician-only care model for the epilepsy quality measures, with superior adherence to the counseling measures of querying for side effects, provision of personalized epilepsy safety education, and screening for behavioral health disorders. The 2 care models performed similarly in all clinical outcomes., Conclusions: An NP-physician team care model employed to increase availability of care could also improve quality of care delivered.

Technological advances are dramatically advancing translational research in Epilepsy. Neurophysiology, imaging, and metadata are now recorded digitally in most centers, enabling quantitative analysis. Basic and translational research opportunities to use these data are exploding, but academic and funding cultures prevent this potential from being realized. Research on epileptogenic networks, antiepileptic devices, and biomarkers could progress rapidly if collaborative efforts to digest this "big neuro data" could be organized. Higher temporal and spatial resolution data are driving the need for novel multidimensional visualization and analysis tools. Crowd-sourced science, the same that drives innovation in computer science, could easily be mobilized for these tasks, were it not for competition for funding, attribution, and lack of standard data formats and platforms. As these efforts mature, there is a great opportunity to advance Epilepsy research through data sharing and increase collaboration between the international research community.

Self-organized criticality (SOC) is a property of complex dynamic systems that evolve to a critical state, capable of producing scale-free energy fluctuations. A characteristic feature of dynamical systems exhibiting SOC is the power-law probability distributions that describe the dynamics of energy release. We show experimental evidence for SOC in the epileptic focus of seven patients with medication-resistant temporal lobe epilepsy. In the epileptic focus the probability density of pathological energy fluctuations and the time between these energy fluctuations scale as (energy) and (time), respectively. The power-laws characterizing the probability distributions from these patients are consistent with computer simulations of integrate-and-fire oscillator networks that have been reported recently. These findings provide insight into the neuronal dynamics of epileptic hippocampus and suggest a mechanism for interictal epileptiform fluctuations. The presence of SOC in human epileptic hippocampus may provide a method for identifying the network involved in seizure generation.

Beginning in the 1970s engineers designed systems to predict epileptic seizures based upon quantitative changes in the electroencephalogram, which they hypothesized began well in advance of clinical seizure onset. These efforts flourished in the 1990s, as independent laboratories demonstrated evidence of a 'preseizure period' up to 20 min prior to clinical symptoms in patients implanted with intracranial electrodes during evaluation for epilepsy surgery. Years later, clinical and laboratory experiments leave little doubt that a preseizure period exists in temporal lobe and perhaps other forms of epilepsy. Its existence, however, raises fundamental questions about what constitutes a seizure, what brain regions are involved in seizure generation, and whether discrete interictal, preictal, ictal and post-ictal physiologies exist, or blend together in a continuous process. Pressing milestones, necessary for clinical utility, are: (1) demonstrating prospective seizure prediction from prolonged human data sets, (2) elucidating mechanisms underlying seizure precursors and (3) implementing these algorithms on implantable hardware platforms. The notion of a preseizure state is catalyzing new clinical and basic science research, which has the potential to dramatically increase our understanding of epilepsy, and to generate exciting new therapies for patients.

The authors compared inferior frontal speech arrest from repetitive transcranial magnetic stimulation (rTMS) with bilateral Wada tests in 17 epilepsy surgery candidates. Although rTMS lateralization correlated with the Wada test in most subjects, rTMS also favored the right hemisphere at a rate significantly greater than the Wada test. Postoperative language deficits were more consistent with Wada results. Available methods for inducing speech arrest with rTMS do not replicate the results of Wada tests.

A personal radon monitor (PRM) has been developed and validated for use in studies of residential 222Rn exposure. A pilot study was performed in six New Jersey homes with elevated Rn concentrations. Occupants wore PRMs, and identical monitors were placed in many rooms of the home. Extensive assessment of PRM performance has been done. A simple model for personal Rn exposure has been tested and measured occupant exposures have been compared with stationary Rn measurements. The PRM works well, with precision limited by the counting error in the exposure range examined (2-100 kBq m-3 h). The PRM sensitivity is 2.2 kBq m-3 h, and its efficiency is 2.6 tracks (kBq m-3 h)-1 per 81 mm2 film.