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.

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, Z_V.

Methods

Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z_V. 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 Z_V 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, Z_V: 91-100%, RR50: 42-80%. Simulated RCT Z_V analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm.

Significance

Z_V may increase the statistical power of an RCT relative to the traditional RR50.

Objective

Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks.

Methods

We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes.

Results

A total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types.

Significance

Quantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.

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.

Gene identification in epilepsy has mainly been limited to large families segregating genes of major effect and de novo mutations in epileptic encephalopathies. Many families that present with common non-acquired focal epilepsies and genetic generalized epilepsies remain unexplained. We assembled a cohort of 'genetically enriched' common epilepsies by collecting and phenotyping families containing multiple individuals with unprovoked seizures. We aimed to determine if specific clinical epilepsy features aggregate within families, and whether this segregation of phenotypes may constitute distinct 'familial syndromes' that could inform genomic analyses. Families with three or more individuals with unprovoked seizures were studied across multiple international centres. Affected individuals were phenotyped and classified according to specific electroclinical syndromes. Families were categorized based on syndromic groupings of affected family members, examined for pedigree structure and phenotypic patterns and, where possible, assigned specific familial epilepsy syndromes. A total of 303 families were assembled and analysed, comprising 1120 affected phenotyped individuals. Of the 303 families, 117 exclusively segregated generalized epilepsy, 62 focal epilepsy, and 22 were classified as genetic epilepsy with febrile seizures plus. Over one-third (102 families) were observed to have mixed epilepsy phenotypes: 78 had both generalized and focal epilepsy features within the same individual (n = 39), or within first or second degree relatives (n = 39). Among the genetic generalized epilepsy families, absence epilepsies were found to cluster within families independently of juvenile myoclonic epilepsy, and significantly more females were affected than males. Of the 62 familial focal epilepsy families, two previously undescribed familial focal syndrome patterns were evident: 15 families had posterior quadrant epilepsies, including seven with occipito-temporal localization and seven with temporo-parietal foci, and four families displayed familial focal epilepsy of childhood with multiple affected siblings that was suggestive of recessive inheritance. The findings suggest (i) specific patterns of syndromic familial aggregation occur, including newly recognized forms of familial focal epilepsy; (ii) although syndrome-specificity usually occurs in multiplex families, the one-third of families with features of both focal and generalized epilepsy is suggestive of shared genetic determinants; and (iii) patterns of features observed across families including pedigree structure, sex, and age of onset may hold clues for future gene identification. Such detailed phenotypic information will be invaluable in the conditioning and interpretation of forthcoming sequencing data to understand the genetic architecture and inter-relationships of the common epilepsy syndromes.