Migraine comorbidity and cognitive performance in patients with focal epilepsy.

BACKGROUND: Migraine and epilepsy are comorbid conditions. While it is well known that epilepsy can have an impact on cognitive abilities, there is conflicting evidence in the literature on the relationship between migraine and cognitive function. The aim of this study was to assess whether migraine comorbidity in patients with newly diagnosed focal epilepsy is associated with cognitive dysfunction. METHODS: This is a post hoc analysis of data prospectively collected for the Human Epilepsy Project (HEP). There were 349 participants screened for migraine with the 13 questions used in the American Migraine Prevalence and Prevention (AMPP) study. Participants were also screened for depression using the Neurological Disorder Depression Inventory for Epilepsy (NDDI-E) and the Center for Epidemiologic Studies Depression Scale (CES-D) and for anxiety using the Generalized Anxiety Disorder-7 (GAD-7) scale. Cognitive performance was assessed with the Cogstate Brief Battery and Aldenkamp-Baker Neuropsychological Assessment Schedule (ABNAS). RESULTS: About a fifth (21.2%) of patients with a new diagnosis of focal epilepsy screened positive for migraine. There were more women and less participants employed full time among the participants with comorbid migraine. They reported slightly more depressive and anxious symptoms than the participants without migraine. Migraine comorbidity was associated with ABNAS memory score (median: 2, range: 0-12, Mann Whitney U p-value: 0.015). However, migraine comorbidity was not associated with Cogstate scores nor ABNAS total scores or other ABNAS domain scores. In linear regressions, depression and anxiety scores were associated with the ABNAS memory score. CONCLUSION: In this study, there was no association between migraine comorbidity and objective cognitive scores in patients with newly diagnosed focal epilepsy. The relationship between migraine comorbidity and subjective memory deficits seemed to be mediated by the higher prevalence of depression and anxiety symptoms in patients with epilepsy with comorbid migraine.

The epilepsy phenome/genome project.

BACKGROUND: Epilepsy is a common neurological disorder that affects approximately 50 million people worldwide. Both risk of epilepsy and response to treatment partly depend on genetic factors, and gene identification is a promising approach to target new prediction, treatment, and prevention strategies. However, despite significant progress in the identification of genes causing epilepsy in families with a Mendelian inheritance pattern, there is relatively little known about the genetic factors responsible for common forms of epilepsy and so-called epileptic encephalopathies. Study design The Epilepsy Phenome/Genome Project (EPGP) is a multi-institutional, retrospective phenotype-genotype study designed to gather and analyze detailed phenotypic information and DNA samples on 5250 participants, including probands with specific forms of epilepsy and, in a subset, parents of probands who do not have epilepsy. RESULTS: EPGP is being executed in four phases: study initiation, pilot, study expansion/establishment, and close-out. This article discusses a number of key challenges and solutions encountered during the first three phases of the project, including those related to (1) study initiation and management, (2) recruitment and phenotyping, and (3) data validation. The study has now enrolled 4223 participants. CONCLUSIONS: EPGP has demonstrated the value of organizing a large network into cores with specific roles, managed by a strong Administrative Core that utilizes frequent communication and a collaborative model with tools such as study timelines and performance-payment models. The study also highlights the critical importance of an effective informatics system, highly structured recruitment methods, and expert data review.

Investigation of patient and observer agreement on description of seizures at initial clinical visit.

There have been few studies of agreement between seizure descriptions obtained from patients and observers. We investigated 220 patients and observers who completed structured questionnaires about patients' semiological seizure features at the initial clinical visit. Inter-rater reliability was assessed using Cohen's kappa and indices of positive and negative agreement. Patients and observers had excellent agreement on the presence of memory impairment and generalized shaking and stiffness during seizures. In addition, patients under-reported seizure descriptions more easily observed externally, whereas observers under-reported change in patient location at seizure end. These findings may guide interpretation of clinical histories obtain in epilepsy care.

Is seizure frequency variance a predictable quantity?

Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions. Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%. Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

Does accounting for seizure frequency variability increase clinical trial power?

OBJECTIVE: Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, ZV. METHODS: Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, ZV. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the ZV method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05). RESULTS: Prediction accuracy across datasets was, ZV: 91-100%, RR50: 42-80%. Simulated RCT ZV analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm. SIGNIFICANCE: ZV may increase the statistical power of an RCT relative to the traditional RR50.