Abstract, published in Epilepsy Research
Objective: Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 hours. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person’s seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a 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 Seizure Tracker™ seizure diary database were used for model development.
Results: Seizure clustering was present in 26.7% of people with epilepsy. Using the proposed model, we found that 37.7-59.4% of seizures identified as clusters based on routine definitions had a high probability of occurring by chance. Several clusters identified by our model were missed by conventional definitions.
Significance: This study proposes a statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through our new model this team has name ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters.