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Abstracts

A Systematic Review of Non-invasive Biomarkers for Seizure Forecasting in Pediatric Epilepsy Patients

Abstract number : 1.315
Submission category : 4. Clinical Epilepsy / 4C. Clinical Treatments
Year : 2025
Submission ID : 546
Source : www.aesnet.org
Presentation date : 12/6/2025 12:00:00 AM
Published date :

Authors :
Presenting Author: Christine Esparza, B.S. – Baylor College of Medicine

Megan Hebdon, Ph.D. D.N.P. R.N. NP-C – University of Utah
Jun Wang, Ph.D. – University of Texas
Yingchao Yuan, M.A. – Dell Medical School
Chumeng Wang, B.S. – Dell Medical School
Rania Agrawal, B.S. – Dell Medical School
Grace Do, B.S., B.A. – Dell Medical School
Avery Bodden, B.A. – Dell Medical School
Galilea Dupree, B.S.N., R.N. – Dell Medical School
Gabriella Gonzalez Ciofuli, B.S. – University of Texas
Halena Rios, B.S. – University of Texas
Jacqueline Rajotte, B.S.Ed. – Barry University
Clifford Calley, M.D. – Dell Medical School

Rationale: Epilepsy is one of the most common neurological disorders globally. While medications, surgical interventions, and dietary changes can be successful in controlling seizures, a subset of individuals experience refractory epilepsy and are at increased risk for sudden unexpected death in epilepsy (SUDEP) [1, 2]. Seizure forecasting efforts have shown variable success using scalp and intracranial electroencephalogram (EEG) measurements, which are restrictive and not practical for daily use [3, 4]. The purpose of this systematic review was to determine non-invasive physiologic and environmental biomarkers which can be used to forecast seizures in pediatric epilepsy patients.

Methods: A systematic search of relevant literature was conducted in PubMed, Web of Science, CINAHL Ultimate, and EMBASE. Articles were reviewed by two investigators in a two-step process with a final sample of 10 articles. One additional article was identified by the lead author through reference checking reviews. Data extraction occurred using two independent extractors to identify the following data points: study characteristics, patient characteristics, and forecasting results. Evidence quality was evaluated by two extractors using the GRADE tool.

Results: The two main study types were algorithmic and correlational, and cardiovascular biomarkers using electrocardiogram (ECG) measurement were the most common. Algorithm anticipation times for seizure onset ranged from 21.8 seconds to 32 minutes, while correlational studies observed cardiovascular biomarker changes 3.59 seconds to 40 minutes before seizure onset. Most of the studies had small sample sizes (n = 7-117), and only three had moderate certainty ratings for evidence quality.

Conclusions: This systematic review provides a comprehensive overview of the current evidence for seizure forecasting. The evidence presented here significantly demonstrates the need for further testing of cardiovascular biomarkers with other physiologic and environmental factors, larger sample size studies, and a precision medicine approach to tailoring algorithms and biomarker measurements to individual patients to achieve more accurate seizure forecasting results in future studies.
References:
1. Sun, X., Lv, Y., & Lin, J. (2023). The mechanism of sudden unexpected death in epilepsy: A mini review. Front Neurol., 14, 1137182. https://doi.org/10.3389/fneur.2023.1137182
2. Ellis, S. P., Jr, & Szabó, C. Á. (2018). Sudden Unexpected Death in Epilepsy: Incidence, Risk Factors, and Proposed Mechanisms. The Am J Forensic Med Pathol., 39(2), 98–102. https://doi.org/10.1097/PAF.0000000000000394
3. Daoud, H., Williams, P., & Bayoumi, M. (2020, 2-16 June 2020). IoT based Efficient Epileptic Seizure Prediction System Using Deep Learning. 2020 IEEE 6th World Forum on Internet of Things (WF-IoT)
4. Shafiezadeh, S., Marco Duma, G., Pozza, M., & Testolin, A. (2024). A systematic review of cross-patient approaches for EEG epileptic seizure prediction. J Neural Eng., 21(6), 10.1088/1741-2552/ad9682. https://doi.org/10.1088/1741-2552/ad9682


Funding: None.

Clinical Epilepsy