Does Natural Variability in Seizure Frequency Matter?

Virtual Special Editions are collections of targeted papers curated by a Guest Editor. Here Dr Daniel M. Goldenholz, MD, PhD 1 Beth Israel Deaconess Medical Center, Boston, US, (author of the Editor’s Choice article in Volume 53) talks about natural variability in seizure frequency.

The natural variability in seizure frequency goes by many names: spontaneous remission, natural fluctuations of disease, unexpected improvement, and so on. The fact is that any name you give, these ups and downs can create great confusion in clinical trials, postsurgical outcomes, medication treatment, and so on.

In this special edition, we present a series of papers published in Seizure: European Journal of Epilepsy exploring how the natural variability in seizure frequency affects different aspects of epilepsy. Recently, our group demonstrated (Goldenholz et al 2017) across three very different datasets that the natural variability in seizure frequency is large enough to cause large “placebo responses” in standard randomized controlled trials. We think this may have specific implications for how trials are planned, conducted and analyzed in the future.

Brodie (2017) recently summarized 1795 people with epilepsy (PWE) treated clinically in Scotland. This article finds consistent patterns of remission, resistance and relapse across decades, and reinforces the concept that perhaps 15% of their cohort are patients who oscillate between relapse and remission, regardless of newer therapies coming to market.

Sillanpää and Schmidt (2017) reviewed a number of natural history studies, and find a consistent two out of three patients will achieve 5-year terminal remission. They too suggest a relative stability in this number over time.

A second paper from Schmidt and Sillanpää (2017) asks the obvious follow-up question: is it safe to take away medications from a patient who is seizure-free? The answers are complicated but very important.

Mohanraj and Brodie (2013) examined what factors can be used to predict seizure outcome in newly diagnosed patients. Their review across a number of studies finds a few constant features that may be valuable predictors.

Hoei-Hansen et al. (2017) reported on the 13 children out of 173 pre-surgical candidates who improved their seizure so much that they were withdrawn from the surgical program. Not knowing how to identify such patients early and having them avoid surgery is a significant problem comprehensive epilepsy centers face.

Elsharkawy et al. (2011) reported on the opposite scenario: patients who were not seizure-free initially after surgery but over 2-10 years became seizure-free. Here too, predicting which epilepsy is expected to “burn out” over time would be incredibly beneficial.