Parental Age and Risk of Epilepsy: A Nationwide Register-Based Study

SIGNIFICANCE: Maternal age and parental age gap, but not paternal age, were associated with the offspring’s risk of epilepsy. Our results do not support the hypothesis that de novo mutations associated with advanced paternal age increase the risk of epilepsy in the offspring.

OBJECTIVE: This study aims to examine the association between maternal age, paternal age, and parental age difference at the time of birth and the risk of epilepsy in the offspring.

METHODS: We carried out a prospective population-based register study of all singletons born in Denmark between 1981 and 2012. Cox regression was used to estimate hazard ratios (HRs) of epilepsy and their corresponding 95% confidence intervals (CIs), adjusted for relevant confounders.

RESULTS: We followed 1,587,897 individuals for a total of ~25 million person-years and identified 21,797 persons with epilepsy during the study period. An excess risk of epilepsy was found in individuals born to mothers younger than 20 years (HR = 1.17, 95% CI = 1.07-1.29) and born to parental couples where paternal age exceeded maternal age by at least 5 years.

The risk of epilepsy increased with increasing parental age gap and was highest when the father was ?15 years older than the mother (adjusted HR = 1.28, 95% CI = 1.16-1.41). In contrast to maternal age, we found that paternal age did not independently contribute to offspring epilepsy risk, once we accounted for the parental age difference (P = .1418). The observed associations with maternal age and parental age gap were invariant to epilepsy subtypes, but were modified by age of epilepsy onset, with the effect being most pronounced in the first 10 years of the child’s life.

Anxiety is Common and Independently Associated with Clinical Features of Epilepsy

CONCLUSION: Anxiety symptoms, often without concomitant depression, were highly prevalent in this epilepsy sample and independently associated with focal/unknown epilepsy and mesial temporal sclerosis. These results strongly support the value of screening specifically for anxiety in the epilepsy clinic, to direct patients to appropriate treatment.

OBJECTIVE: The objective of this study was to assess for independent association of anxiety symptoms with epilepsy localization and other epilepsy-related and demographic factors in a large tertiary care adult epilepsy population.

METHODS: Among 540 adults, anxiety was measured by the Symptom Checklist 90-R (SCL-90R) anxiety subscale, and detailed demographics, epilepsy localization, and depression scores (SCL-90R) were collected. High anxiety was defined by SCL-90R anxiety T-score???60. Stepwise multiple logistic regression was carried out to assess for independent association of high anxiety scores with demographic and clinical factors.

RESULTS: High anxiety symptoms were present in 46.1% of participants (N?=?250). Focal or unknown epilepsy type and depression scores were independently associated with high anxiety (adjusted odds ratios (OR): 2.89 (95% confidence interval [CI]?=?1.33-6.29, p?=?0.007) and 2.12 (95% CI?=?1.83-2.45, p?<?0.001), respectively; depression odds per 5-point increase in scale). Among the focal epilepsy subpopulation, mesial temporal sclerosis was also independently associated with high anxiety, with adjusted OR: 2.12 (95% CI?=?1.11-4.04, p?=?0.023).

Lower education, non-white race/ethnicity, Spanish native language, prior head trauma, antiseizure drug polytherapy, and left focus or bilateral foci (in focal epilepsy) were associated with high anxiety in simple logistic regression, but these associations were not independent. A total of 46 individuals (18.4% of those with high anxiety) scored high for anxiety but not depression. Only 26% of those with high anxiety symptoms were taking a potentially anxiolytic medication.

Generic Antiepileptic Drugs – Safe or Harmful in Patients with Epilepsy?

Generic antiepileptic drugs (AED) are significantly cheaper than brand name drugs, and may reduce overall health care expenditures. Regulatory bodies in Europe and North America require bioequivalence between generic and innovator drugs with regard to area under the plasma concentration–time curve (AUC) and peak plasma concentration (Cmax); strict cutoff values have been defined. The main issue is if bioequivalence ensures therapeutic equivalence. Are switches from brand to generic, or between generic AEDs entirely safe or potentially harmful in patients with epilepsy?

We summarized and evaluated the available evidence from bioequivalence, health care utilization, and clinical studies on safety of generic AEDs. In most cases, variations in AUC and Cmax were negligible when comparing innovator and generic AEDs. Due to interindividual pharmacokinetic and pharmacodynamic variability, measured differences between innovator and generic drugs may be the same as differences between different lots of the same brand. Studies from several countries based on insurance data have reported an increase in health care usage after switch from brand to generic AEDs; switchback rates are significantly higher for AEDs compared to other compounds. Patients may be confused, and nonadherence may increase, when AEDs are switched between manufacturers, perhaps due to changes in medication shape and color. But clinical studies do not report changes in seizure frequency and tolerability attributable to generics.

Sufficient evidence indicates that most generics are bioequivalent to innovator AEDs; they do not pose a relevant risk for patients with epilepsy. However, some patients are reluctant towards variations in color and shape of their AEDs which may result in nonadherence. We recommend administering generics when a new AED is initiated. Switches from brand to generic AEDs for cost reduction and between generics, which is rarely required, generally seem to be safe, but should be accompanied by thorough counseling of patients on low risks.

New Light Shed on Mechanisms of Pediatric Epilepsy

Research by Cardiff University has uncovered the brain activity that underlies absence epilepsy, offering new hope for the development of innovative therapies for this disabling disease. Absence epilepsy—the most common form of epilepsy in children and teenagers—causes episodes of lack of awareness which are often mistaken for daydreaming. The brain activity that causes this form of epilepsy has remained poorly understood, until recent research has observed this activity for the first time.

An international team of researchers led by Professor Vincenzo Crunelli, from Cardiff University’s School of Biosciences, investigated the types of electrical activity that occurred in the brains of mice during an absence seizure.

Professor Crunelli said: “Although the origin of absence epilepsy remains poorly understood, we do know that if we monitor the electrical activity in the brain during a seizure, we see peaks in the activity called spike and wave discharges.

“We also know that synchronous activity in a part of the brain called the thalamocortical network, which is organised in a feedback loop, underlies the appearance of these spike-wave discharges.

“But the relationship between the brain cell activities in this loop, and how these relationships lead to the brain activity in absence seizures, is strongly debated.”

The group of researchers from Cardiff University, University of Malta, Centre National de la Recherche Scientifique and the University of Szeged in Hungary, simultaneously recorded brain activity between several different brain areas during absence seizures for the first time. This allowed them to observe the relationships between the different regions of the brain during an absence seizure, and they found that it played a role in the presence of the spike and wave discharges.

Incidence of Sudden Unexpected Death in Epilepsy in Children is Similar to Adults

Objective: To determine the incidence of sudden unexpected death in epilepsy (SUDEP) in children in Ontario, Canada.

Methods: Cases of suspected pediatric SUDEP occurring between January 1, 2014, and December 31, 2015, in Ontario, Canada, were eligible for inclusion. Potential cases were identified through 3 sources: a national pediatrician surveillance program, child neurologist report, and screening of provincial forensic autopsies. Cases were classified as definite, definite plus, probable, possible, and near/near plus according to criteria described by Nashef et al. (Epilepsia 2012).

Overall crude pediatric SUDEP incidence and the incidence of definite or probable pediatric SUDEP were calculated using estimates of the prevalence of pediatric epilepsy in Canada drawn from government survey data and the number of children living in Ontario. Capture-recapture analysis was used to estimate the number of missing cases and determine an adjusted definite/probable SUDEP incidence.

Results: Seventeen cases of pediatric SUDEP resulted in an overall incidence of 1.17 (95% confidence interval 0.68–1.88) per 1,000 pediatric epilepsy person-years. The definite/probable incidence, including definite (n = 11), definite plus (n = 2), or probable (n = 3) SUDEP cases, was 1.11 (0.63–1.79). Capture-recapture analysis indicated an estimated 21 (16–39) definite/probable SUDEP cases occurred during the study period, giving an adjusted incidence of definite/probable SUDEP of 1.45 (0.90–2.22) per 1,000 pediatric epilepsy person-years.

Conclusion: SUDEP may be more common in children than widely reported, with the incidence rate of definite/probable SUDEP in children being similar to rates reported in adults.

Seizure Detection Using Scalp-EEG

Scalp electroencephalography (EEG)-based seizure-detection algorithms applied in a clinical setting should detect a broad range of different seizures with high sensitivity and selectivity and should be easy to use with identical parameter settings for all patients. Available algorithms provide sensitivities between 75% and 90%. EEG seizure patterns with short duration, low amplitude, circumscribed focal activity, high frequency, and unusual morphology as well as EEG seizure patterns obscured by artifacts are generally difficult to detect. Therefore, detection algorithms generally perform worse on seizures of extratemporal origin as compared to those of temporal lobe origin.

Specificity (false-positive alarms) varies between 0.1 and 5 per hour. Low false-positive alarm rates are of critical importance for acceptance of algorithms in a clinical setting. Reasons for false-positive alarms include physiological and pathological interictal EEG activities as well as various artifacts. To achieve a stable, reproducible performance (especially concerning specificity), algorithms need to be tested and validated on a large amount of EEG data comprising a complete temporal assessment of all interictal EEG. Patient-specific algorithms can further improve sensitivity and specificity but need parameter adjustments and training for individual patients. Seizure alarm systems need to provide on-line calculation with short detection delays in the order of few seconds.

Scalp-EEG-based seizure detection systems can be helpful in an everyday clinical setting in the epilepsy monitoring unit, but at the current stage cannot replace continuous supervision of patients and complete visual review of the acquired data by specially trained personnel. In an outpatient setting, application of scalp-EEG-based seizure-detection systems is limited because patients won’t tolerate wearing widespread EEG electrode arrays for long periods in everyday life. Recently developed subcutaneous EEG electrodes may offer a solution in this respect.

Standards for Testing and Clinical Validation of Seizure Detection Devices

To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0-4).

We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices.

Prediction Method for Epileptic Seizures Developed

Epileptic seizures strike with little warning and nearly one third of people living with epilepsy are resistant to treatment that controls these attacks. More than 65 million people worldwide are living with epilepsy. Now researchers at the University of Sydney have used advanced artificial intelligence and machine learning to develop a generalised method to predict when seizures will strike that will not require surgical implants.

In a paper published this month in Neural Networks, Dr. Kavehei and his team have proposed a generalized, patient-specific, seizure-prediction method that can alert epilepsy sufferers within 30 minutes of the likelihood of a seizure. Dr. Kavehei said there had been remarkable advances in artificial intelligence as well as micro- and nano-electronics that have allowed the development of such systems. “Just four years ago, you couldn’t process sophisticated AI through small electronic chips. Now it is completely accessible. In five years, the possibilities will be enormous,” Dr. Kavehei said.

Affiliate Stigma and Caregiver Burden in Intractable Epilepsy

Intractable epilepsy can be challenging for patients and for their families. Disability rates in patients are high, causing tremendous physical and emotional burden on family caregivers. Additionally, caregivers may experience affiliate stigma, where they perceive and internalize the negative societal views of a condition and exhibit a psychological response. Affiliate stigma has been rarely studied in caregivers of those with intractable epilepsy.

This study examined the relationship between affiliate stigma and the levels of burden experienced by caregivers, as well as how these levels may vary between those caring for children and adults. This cross-sectional approach used a self-administered survey offered to caregivers of family members with confirmed diagnoses of intractable epilepsy.

We measured burden with the 30-item Carer’s Assessment of Difficulties Index (CADI) and affiliate stigma with a six-item scale examining caregivers’ perceptions of stigma directed toward themselves and their family members with epilepsy. Four nested ordinary-least-squares regression models were estimated using stigma scale scores to predict levels of perceived burden adjusting for demographic variables. Age of the patient with epilepsy was dichotomized (pediatric/adult) to assess a possible moderating effect of patient’s age on the relationship between stigma and caregiver burden. Respondents (N = 136) were predominantly White (83%), female (75%), and married (69%), with an average age of 43 years. Patients with epilepsy were 52% male with ages ranging from 2 to 82 years.

Each of the regression models yielded positive associations (p < 0.001) between perceived levels of caregiver burden and affiliate stigma. Additionally, the age of the family member with epilepsy moderated (p < 0.05) the effect, with the relationship stronger for caregivers of adults. In a highly select group of patients with refractory epilepsy recruited mostly from a cannabidiol (CBD) clinic, this study demonstrated that caregivers experience affiliate stigma, which is significantly associated with higher burden levels. Additionally, this study identified specific needs, which when met, may improve caregivers’ physical and mental health.

Heart Rate Variability in Epilepsy: A Potential Biomarker of Sudden Unexpected Death in Epilepsy Risk [Study Featuring the Work of CURE Grantee, Kenneth Myers]

Significance: These findings suggest that autonomic dysfunction is associated with Sudden Unexpected Death in Epilepsy (SUDEP) risk in patients with epilepsy due to sodium channel mutations. The relationship of heart rate variability to SUDEP merits further study; heart rate variability may eventually have potential as a biomarker of SUDEP risk, which would allow for more informed counseling of patients and families, and also serve as a useful outcome measure for research aimed at developing therapies and interventions to reduce SUDEP risk.

Objective: SUDEP is a tragic and devastating event for which the underlying pathophysiology remains poorly understood; this study investigated whether abnormalities in heart rate variability (HRV) are linked to SUDEP in patients with epilepsy due to mutations in sodium channel (SCN) genes.

Methods: We retrospectively evaluated HRV in epilepsy patients using electroencephalographic studies to study the potential contribution of autonomic dysregulation to SUDEP risk. We extracted HRV data, in wakefulness and sleep from 80 patients with drug?resistant epilepsy, including 40 patients with mutations in SCN genes and 40 control patients with non?SCN drug?resistant epilepsy. From the SCN group, 10 patients had died of SUDEP. We compared HRV between SUDEP and non?SUDEP groups, specifically studying awake HRV and sleep:awake HRV ratios.

Results: The SUDEP patients had the most severe autonomic dysregulation, showing lower awake HRV and either extremely high or extremely low ratios of sleep?to?awake HRV in a subgroup analysis. A secondary analysis comparing the SCN and non?SCN groups indicated that autonomic dysfunction was slightly worse in the SCN epilepsy group.