Modeling seizure self‐prediction: An e‐diary study

Abstract

Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy.

Forecasting cycles of seizure likelihood

“…Furthermore, many seizure triggers that have been shown to be useful biomarkers of seizure likelihood can be measured noninvasively. For instance, self‐reported stress is predictive of seizures 27,28 . Heart rate 29,30 and other physiological signals monitored from a smartwatch device 31 have also been used to forecast seizure likelihood.…”

The benefits of electronic diaries in understanding the experience of health

“…Considerable diversity has evolved over the past decade in how electronic diaries are structured, in how frequently they are completed, and in the health phenomena they are used to study. As evidenced by their use to understand sexual behaviour,4 smoking cessation5 and substance use,6 asthma management7 seizure prediction,8 psychological state and disease symptomatology in old age,9 paediatric arthritis pain,10 and emotional regulation,11 electronic diaries can be a flexible and powerful way to intimately connect to the health experiences of others.…”

A method for identifying associations between seizures and possible trigger events in adults with intellectual disability

“…Second, some researchers have raised doubts regarding the trustworthiness of data collected using pen‐and‐paper diaries, instead advocating electronic diaries . These allow time‐stamping of diary entries and greater control over when diaries are completed, thereby reducing the potential for retrospective reporting and recall bias . We considered using electronic diaries, but consultation with carers revealed significant feasibility concerns.…”