The role of EEG in predicting post-stroke seizures and an updated prognostic model (SeLECT-EEG)
Authors: Schubert Kai Michael , Dasari Vijaya , Oliveira Ana Lúcia , Tatillo Chiara , Naeije Gilles , Strzelczyk Adam , Gaspard Nicolas , Galovic Marian , Punia Vineet , Bentes Carla ,   the SeLECT Collaborators
Published: December 2024

Importance

Seizures significantly impact outcomes after stroke, underscoring the need for accurate predictors of post-stroke epilepsy.

Objective

To evaluate whether electrographic biomarkers detected early after acute ischemic stroke enhance the prediction of post-stroke epilepsy.

Design

Multicenter cohort study with data collected from 2002 to 2022 and final data analysis completed in July 2024.

Setting

Eleven international cohorts from tertiary referral centers, six with available EEG data.

Participants

1,105 stroke survivors with neuroimaging-confirmed ischemic stroke (mean age 71, 54% male) who underwent EEG within the first 7 days post-stroke.

Exposure

Presence of electrographic biomarkers detected through EEG.

Main Outcome and Measures

Occurrence of post-stroke epilepsy. The impact of electrographic biomarkers on the risk of post-stroke epilepsy was assessed using Cox proportional hazards regression, adjusted through inverse probability weighting.

Results

Among 1,105 participants, 119 (11%) developed post-stroke seizures. Epileptiform activity (lateralized periodic discharges, interictal epileptiform discharges, and electrographic seizures; (odds ratio [OR] 2.0, 95% confidence interval [CI]: 1.3-3.0, p=0.001)) and regional slowing (OR 1.9, 95% CI: 1.2-2.9, p=0.004) were independently associated with developing post-stroke epilepsy. The novel SeLECT-EEG prognostic model, specifically developed for stroke survivors without acute symptomatic seizures (ASyS),, outperformed the previous gold-standard model (SeLECT2.0; 0.71 [95% CI: 0.65-0.76]) with a concordance statistic of 0.75 (95% CI: 0.71-0.80; p < 0.001).

Conclusions and Relevance

Electrographic findings significantly enhance the prediction of post-stroke epilepsy beyond previously known clinical risk factors and may serve as prognostic biomarkers. The integration of these biomarkers into the SeLECT-EEG model in patients without acute symptomatic seizures provides a more accurate prognostic tool for early post-stroke epilepsy prediction.

Key points

Question

Can early detection of electrographic biomarkers after acute ischemic stroke improve the prediction of post-stroke epilepsy?

Findings

Among 1,105 stroke survivors who received early EEG (≤ 7 days after stroke), post-stroke seizures occurred in 119 (11%). Stroke survivors with epileptiform activity had a 42% risk (95% CI 30%-49%) of developing post-stroke epilepsy 5 years after stroke, compared to a 13% risk (95% CI 9%-16%) in those without. Additionally, the 5-year risk of post-stroke epilepsy was twice as high in those with regional slowing (24%, 95% CI 18%-29%) compared to those without it (11%, 95% CI 5%-15%). Beyond known clinical risk factors, epileptiform activity and regional slowing were independently associated with developing post-stroke epilepsy. We integrated these findings into a novel prognostic model (SeLECT-EEG; concordance statistic 0.75 [95% CI: 0.71-0.80]), which outperformed the previous gold-standard model (SeLECT2.0; concordance statistic 0.71 [95% CI: 0.65-0.76]; p < 0.001).

Meaning

Early electrographic biomarkers improve the prediction of post-stroke epilepsy and may inform counseling and management strategies for stroke survivors at risk of seizures.