Individualizing the Definition of Seizure Clusters Based on Temporal Clustering Analysis
Abstract number :
2.093
Submission category :
4. Clinical Epilepsy / 4A. Classification and Syndromes
Year :
2019
Submission ID :
2421541
Source :
www.aesnet.org
Presentation date :
12/8/2019 4:04:48 PM
Published date :
Nov 25, 2019, 12:14 PM
Authors :
Sharon Chiang, UCSF; Sheryl Haut, Montefiore Medical Center; Victor Ferastraoaru, Montefiore Medical Center; Vikram R. Rao, University of California, San Francisco; Maxime Baud, University Hospital Bern; William H. Theodore, NINDS; Robert Moss, Seizure Tr
Rationale: Seizure clusters are often encountered in people with severe or poorly controlled epilepsy. Detection of seizure clusters usually is based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 hours. There is no uniform consensus on how to define seizure clusters. Current definitions also fail to distinguish between statistically significant clusters and those resulting from natural variation. We evaluate current cluster definitions using one of the world's largest seizure diary databases, and propose an alternative principled statistical approach to defining seizure clusters that addresses these issues. Methods: A total of 533,968 clinical seizures from 1,748 people with epilepsy in the SeizureTracker.com seizure diary database were analyzed. The Hurst exponent, a statistical measure of time-series memory, was calculated for each seizure diary as a standardized measure of seizure clustering. Change-point analysis (CPA) was used to identify statistical seizure clusters at varying levels of evidence for being unlikely to have resulted from chance. Clusters defined by CPA were compared to those identified by routine seizure cluster definitions. Results: Seizure clustering was present in 26.7% (95% CI, 24.5-28.7%) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. We found that 37.7-59.4% of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified using CPA were missed by conventional definitions. Conclusions: We propose a new individualized definition of seizure clusters that accounts for individual variation in seizure frequency and evaluates statistical significance. Several clusters were identified that evaded classical cluster definitions. A large proportion of events classically classified as clusters were likely to have resulted by chance. This new definition has the potential to improve cluster treatment by identifying previously unrecognized clusters and preventing unnecessary treatment. Funding: No funding
Clinical Epilepsy