Post-stroke seizure risk prediction models: a systematic review and meta-analysis

Seong Hoon Lee* (Corresponding Author), Kah Long Aw, Snehashish Banik, Phyo Kyaw Myint

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Objectives: Stroke is the commonest cause of epileptic seizures in older adults. Risk factors for post-stroke seizure (PSS) are well known, however predicting PSS risk is clinically challenging. This study aims to evaluate the predictive accuracy of PSS risk prediction models developed to date.Methods: We performed a systematic review and meta-analysis of studies using MEDLINE and EMBASE from database inception to 28th December 2020. The search criteria included all peer-reviewed research articles, developing or validating PSS risk prediction models for ischemic and/or hemorrhagic stroke. Random-effects meta-analysis was used to generate summary statistics of model performance and receiver operating characteristic curves.Quality appraisal of studies was conducted using PROBAST.Results: Thirteen original studies involving 182,673 stroke patients (mean age: 38 – 74.9 years, 29.4% – 60.9% males), reporting fifteen PSS risk prediction models were included. The incidence of early PSS (occurring ≤ 1 week from stroke onset) and late PSS (occurring > 1week from stroke onset) was 4.5% and 2.1%, respectively. Cortical involvement, functional deficits, increasing lesion size, early seizures, decreasing age, and haemorrhage were the commonest predictors across the models. SeLECT demonstrated greatest predictive Post-stroke seizure risk prediction models 33 accuracy (AUC 0.77 [95%CI: 0.71 – 0.82]) for late PSS in ischemic stroke, and CAVE for predicting late PSS in hemorrhagic stroke (AUC 0.81 [0.76 – 0.86]). Fourteen of fifteen studies demonstrated high risk of bias, with lack of model validation and reporting of performance measures on calibration and discrimination being the commonest reasons.Significance: Although risk factors for PSS are widely documented, this review identified few multivariate models with low risk of bias synthetising single variables into an individual prediction of seizure risk. Such models may help personalise clinical management and serve useful research tools by identifying stroke patients at high risk of developing PSS for recruitment into studies of anti-epileptic drug prophylaxis.
Original languageEnglish
Pages (from-to)302-14
Number of pages12
JournalEpileptic Disorders
Issue number2
Early online date6 Dec 2021
Publication statusPublished - 1 Apr 2022


  • Cerebrovascular Disorders
  • Epilepsy
  • Seizure
  • Stroke
  • Systematic Review


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