Epilepsy is a common childhood neurologic disorder. In 2007, epilepsy affected an estimated 450,000 children aged 0-17 years in the United States. Approximately 53% of children with epilepsy and special health care needs have co-occurring conditions, and only about one third have access to comprehensive care. The few studies of mortality risk among children with epilepsy as compared with the general population generally find a higher risk for death among children with epilepsy with co-occurring conditions but a similar risk for death among children with epilepsy with no co-occurring conditions. However, samples from these mortality studies are often small, limiting comparisons, and are not representative. This highlights the need for expanded mortality surveillance among children with epilepsy to better understand their excess mortality. This report describes mortality among children with epilepsy in South Carolina during 2000-2011 by demographic characteristics and underlying causes of death. The overall mortality rate among children with epilepsy was 8.8 deaths per 1,000 person-years, and the annual risk for death was 0.84%. Developmental conditions, cardiovascular disorders, and injuries were the most common causes of death among children with epilepsy. Team-based care coordination across medical and nonmedical systems can improve outcomes and reduce health care costs for children with special health care needs, but they require more study among children with epilepsy. Ensuring appropriate and timely health care and social services for children with epilepsy, especially those with complications, might reduce the risk for premature death. Health care providers, social service providers, advocacy groups and others can work together to assess whether coordinated care can improve outcomes for children with epilepsy.
Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.
The goal of the project is to determine characteristics of academic neurophysiologist EEG interpreters (EEGers), which predict good interrater agreement (IRA) and to determine the number of EEGers needed to develop an ideal standardized testing and training data set for epileptiform transient (ET) detection algorithms.A three-phase scoring method was used. In phase 1, 19 EEGers marked the location of ETs in two hundred 30-second segments of EEG from 200 different patients. In phase 2, EEG events marked by at least 2 EEGers were annotated by 18 EEGers on a 5-point scale to indicate whether they were ETs. In phase 3, a third opinion was obtained from EEGers on any inconsistencies between phase 1 and phase 2 scoring.The IRA for the 18 EEGers was only fair. A select group of the EEGers had good IRA and the other EEGers had low IRA. Board certification by the American Board of Clinical Neurophysiology was associated with better IRA performance but other board certifications, years of fellowship training, and years of practice were not. As the number of EEGers used for scoring is increased, the amount of change in the consensus opinion decreases steadily and is quite low as the group size approaches 10.The IRA among EEGers varies considerably. The EEGers must be tested before use as scorers for ET annotation research projects. The American Board of Clinical Neurophysiology certification is associated with improved performance. The optimal size for a group of experts scoring ETs in EEG is probably in the 6 to 10 range.
Despite clinical evidence that thyroid hormone is essential for brain development before birth, effects of thyroid hormone on the fetal brain have been largely unexplored. One mechanism of thyroid hormone action is regulation of gene expression, because thyroid hormone receptors (TRs) are ligand-activated transcription factors. We used differential display to identify genes affected by acute T 4 administration to the dam before the onset of fetal thyroid function. Eight of the 11 genes that we identified were selectively expressed in brain areas known to contain TRs, indicating that these genes were directly regulated by thyroid hormone. Using in situ hybridization, we confirmed that the cortical expression of both neuroendocrine-specific protein (NSP) and Oct-1 was affected by changes in maternal thyroid status. Additionally, we demonstrated that both NSP and Oct-1 were expressed in the adult brain and that their responsiveness to thyroid hormone was retained. These data are the first to identify thyroid hormone-responsive genes in the fetal brain.
Summary Surgical resection of the hippocampus is the most successful treatment for medication‐refractory medial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis. Unfortunately, at least one of four operated patients continue to have disabling seizures after surgery, and there is no existing method to predict individual surgical outcome. Prior to surgery, patients who become seizure free appear identical to those who continue to have seizures after surgery. Interestingly, newly converging presurgical data from magnetic resonance imaging (MRI) and intracranial electroencephalography (EEG) suggest that the entorhinal and perirhinal cortices may play an important role in seizure generation. These areas are not consistently resected with surgery and it is possible that they continue to generate seizures after surgery in some patients. Therefore, subtypes of MTLE patients can be considered according to the degree of extrahippocampal damage and epileptogenicity of the medial temporal cortex. The identification of these subtypes has the potential to drastically improve surgical results via optimized presurgical planning. In this review, we discuss the current data that suggests neural network damage in MTLE, focusing on the medial temporal cortex. We explore how this evidence may be applied to presurgical planning and suggest approaches for future investigation.
