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Characterising seizure cycles in pediatric epilepsy
Hannah Kamitakahara1, Philippa J Karoly2, Rachel E Stirling2
1Seer Medical Pty Ltd, VIC, Australia.
Insights
Multiday seizure cycles, similar in children and adults with epilepsy, can predict seizure timing. These findings offer a new way to manage epilepsy unpredictability in pediatric patients.
Area of Science:
- Epilepsy research
- Chronobiology
- Pediatric neurology
Background:
- Multiday cyclic patterns in seizure timing are known in adults with epilepsy.
- Longer-term seizure patterns in pediatric epilepsy cohorts remain underexplored.
- This study investigates multiday seizure cycles in pediatric and adult epilepsy patients.
Purpose of the Study:
- To identify and compare multiday seizure cycles between pediatric and adult epilepsy cohorts.
- To validate cycle-based methods for estimating seizure likelihood in pediatric epilepsy.
Main Methods:
- Retrospective analysis of multiday seizure cycles from 325 electronic seizure diary users (71 pediatric).
- K-means clustering grouped cycles; synchronization index quantified cycle strength.
- Rayleigh test identified significant cycles; Wilcoxon rank-sum test compared cohorts; AUC calculated for model validation.
Main Results:
- Analysis of 30,019 seizures revealed significant circadian, weekly, fortnightly, and monthly seizure cycles.
- Prevalence and strength of these cycles did not differ between pediatric and adult cohorts.
- Cycle-based models reliably predicted seizure occurrence, outperforming random models and moving average models.
Conclusions:
- Multiday seizure cycles derived from seizure diaries are a viable method for understanding pediatric epilepsy patterns.
- Identifying individual seizure cycles can decrease seizure unpredictability.
- This approach has potential to inform clinical decision-making for epilepsy management.
Background:
Multiday cyclic patterns underlying the timing of seizures are well-established in adults with epilepsy. However, longer-term patterns underpinning these models are yet to be explored extensively in pediatric cohorts. This study aims to identify and compare multiday seizure cycles between pediatric and adult cohorts, followed by a preliminary validation of cycle-based methods for estimating seizure likelihood in a pediatric cohort.
Methods:
Multiday seizure cycles were extracted retrospectively from 325 (71 pediatric) electronic seizure diary users with confirmed epilepsy. Cycles were grouped (k-means clustering) and seizure cycles quantified (synchronisation index) with significant cycles identified (Rayleigh test (p < 0.05)). Wilcoxon rank-sum test assessed differences in prevalence and strength of cycle groups between pediatric and adult cohorts. The accuracy of cycle-based models to track pediatric seizure occurrence was calculated from the receiver operating characteristic (area under the curve; AUC) comparing estimated cycles to shuffled surrogate data and further validated with a moving average model.
Findings:
30,019 seizures (pediatric: Median = 51, IQR (Q1 = 30, Q3 = 115), Range (21-661), adult: Median = 46, IQR (Q1 = 31, Q3 = 93), Range (20-1112) were analysed and seizure cycles grouped across circadian (0.5-1.5 days), about-weekly (2-12 days), about-fortnightly (13-22 days) and about-monthly (23-32 days) periodicities. Significant cycles were identified in each cycle group, with no differences in prevalence or cycle strength between pediatric and adult cohorts. Estimated cycles showed a reliable assessment of observed seizure occurrence (significantly (p < 0.05) better performance compared to random models for 88% (44 of 50) and moving average models for 50% (25 of 50) of observed daily seizure occurrence).
Significance:
Multiday seizure cycles estimated from seizure diaries present a viable model for identifying longer-term seizure patterns in a pediatric cohort. Knowledge of these individual seizure cycles has potential to reduce the unpredictability of seizure timing and inform clinical decision-making.
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