Monitoring the frequency and duration of epileptic seizures: "A journey through time"

Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing

OBJECTIVE

A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. In this study, we introduce and validate "pro-ictal EEG scheduling", a method to schedule vEEG monitoring to coincide with periods of increased seizure likelihood as a low-risk approach to enhance the diagnostic yield.

METHODS

A database of long-term ambulatory vEEG monitoring sessions (n = 5,038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events) to generate cycle-based estimates of seizure risk. In adults, vEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (n = 305) and compared to remaining studies (baseline: n = 3,586). Test of proportions and risk-ratios (RR) were applied to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure, and diary event) during monitoring. The impact of clinical and demographic factors (age, sex, epilepsy-type, and medication) was also explored.

RESULTS

During vEEG monitoring, the high-risk group was significantly more likely to have an abnormal vEEG report (190/305:62% vs 1,790/3,586:50% [%change = 12%], RR = 1.25, 95% confidence interval [CI] = [1.137-1.370], p < 0.001), present with a confirmed seizure (56/305:18% vs 424/3,586:11% [%change = 7%], RR = 1.63, 95% CI = [1.265-2.101], p < 0.001) and report an event (153/305:50% vs 1,267/3,586:35% (%change = 15%), RR = 1.420, 95% CI = [1.259:1.602], p < 0.001). Similar effects were observed across clinical and demographic features.

INTERPRETATION

This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. ANN NEUROL 2024;96:1148-1159.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy

OBJECTIVE

To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy.

METHODS

Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication.

RESULTS

Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients.

SIGNIFICANCE

There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

The cost-effectiveness of a real-time seizure detection application for people with epilepsy

OBJECTIVES

Automated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore.

METHODS

We used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed.

RESULTS

At a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty.

CONCLUSIONS

This early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.