Abstract Details

Title Seizures and Epilepsy after Ischemic stroke: A Systematic Review of Prognostic Studies

Topic Epilepsy/Clinical Neurophysiology (EEG)

Presentation(s) P9 - Poster Session 9 (5:00 PM-6:00 PM)

Poster/Presentation
Number 11-005

Objective

To systematically review and meta-analyze prognostic factors and predictive models for seizures and epilepsy following ischemic stroke.

Background

Ischemic stroke is the leading cause of acquired epilepsy in adults. Post-stroke seizures and post-stroke epilepsy (PSE) significantly worsen morbidity, mortality, and quality of life, yet prognostic models remain inconsistently validated.

Design/Methods A systematic search was conducted in PubMed, Embase, Web of Science, and Scopus through August 2025. Eligible studies included observational cohorts and case–control designs evaluating adult patients with ischemic stroke and reporting seizures or epilepsy as outcomes. Data extraction followed the CHARMS framework, and risk of bias was assessed with PROBAST. Random-effects meta-analyses with Hartung–Knapp adjustment were performed for prognostic factors and model discrimination, synthesizing odds ratios (OR) and area under the curve (AUC) values.

Results

Eight studies met inclusion criteria, comprising multicenter registries and prospective cohorts. Several prognostic factors were consistently associated with PSE, including early seizures within 7 days (OR 4.04, 95% CI 1.80–9.05), higher NIHSS scores (OR 4.01, 95% CI 2.12–7.59), and biomarker abnormalities such as elevated Endostatin, reduced Hsc70, and low TNF-R1. Electroencephalographic alterations, including epileptiform activity (OR 2.00, 95% CI 1.32–3.04), also conferred increased risk. The meta-analysis of model discrimination showed a pooled AUC of 0.739 (95% CI 0.703–0.773), with negligible heterogeneity (I² = 0%).

Conclusions Prognostic evidence supports a multidimensional approach combining clinical severity, acute seizures, EEG abnormalities, and biomarkers for risk stratification. Current models demonstrate moderate predictive performance, highlighting the need for external validation and refinement through multimodal and machine learning approaches to improve personalized risk prediction in stroke survivors.

Authors/Disclosures
Natanael Duarte, MD PRESENTER	Dr. Duarte has nothing to disclose.
Eugenio F. Roman Cruz, Sr., MD	Dr. Roman Cruz has nothing to disclose.
Mauricio Megchún Hernández, PhD	Dr. Megchún Hernández has nothing to disclose.
Miguel Angel Arce Huamani, PhD	Dr. Arce Huamani has nothing to disclose.
Kevin R. Quizhpi, Sr., Odontólogo	Dr. Quizhpi has nothing to disclose.
Camila J. Vásquez avila, Sr., Dentist	Dr. Vásquez avila has nothing to disclose.
Oriana Rivera-Lozada (Vicerrectorado de Investigación, Universidad Señor de Sipán, Chiclayo)	No disclosure on file
Cesar Bonilla-Asalde (Escuela de Postgrado, Universidad Señor de Sipán, Chiclayo)	No disclosure on file
Joshuan J. Barboza (Escuela de Medicina, Universidad Señor de Sipán, Chiclayo)	No disclosure on file

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