Is seizure frequency variance a predictable quantity?. Issue 2 (9th January 2018)

• Record Type: Journal Article
• Authors: Goldenholz, Daniel M.; Goldenholz, Shira R.; Moss, Robert; French, Jacqueline; Lowenstein, Daniel; Kuzniecky, Ruben; Haut, Sheryl; Cristofaro, Sabrina; Detyniecki, Kamil; Hixson, John; Karoly, Philippa; Cook, Mark; Strashny, Alex; Theodore, William H.
• Is Part Of: Annals of clinical and translational neurology. Volume 5:Issue 2(2018)
• Page Start: 201
• Page End: 207
• Publication Date: 2018-01-09
• Subjects: Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1002/acn3.519
• Languages: English
• ISSNs: 2328-9503

Abstract

Abstract: 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 ( R 2 > 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.