Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

Prospective validation of a seizure diary forecasting falls short.

OBJECTIVE: Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS: We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI. RESULTS: The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (non-verified) diaries (with presumed under-reporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE: The previously developed AI forecasting tool did not outperform a very simple moving average forecasting this prospective cohort, suggesting that the AI model should be replaced.

Exercise, medication adherence, and the menstrual cycle: How much do these change seizure risk?

People with epilepsy can experience tremendous stress from the uncertainty of when a seizure will occur. Three factors deemed important because of their potential influence on seizure risk are exercise, medication adherence, and the menstrual cycle. A narrative review was conducted through PubMed searching for relevant articles on how seizure risk is modified by 1) exercise, 2) medication adherence, and 3) the menstrual cycle. There was no consensus about the impact of exercise on seizure risk. Studies about medication nonadherence suggested an increase in seizure risk, but there was not a sufficient amount of data for a definitive conclusion. Most studies about the menstrual cycle reported an increase in seizures connected to a specific aspect of the menstrual cycle. No definitive studies were available to quantify this impact precisely. All three triggers reviewed had gaps in the research available, making it not yet possible to definitively quantify a relationship to seizure risk. More quantitative prospective studies are needed to ascertain the extent to which these triggers modify seizure risk.