Post‐stroke seizure risk prediction models: a systematic review and meta‐analysis. Issue 2 (7th June 2022) - Lee, Seong Hoon; Aw, Kah Long; Banik, Snehashish; Myint, Phyo Kyaw
Abstract: Objective. 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 aimed 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 28 th December 2020. The search criteria included all peer‐reviewed research articles, in which PSS risk prediction models were developed or validated for ischaemic and/or haemorrhagic 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 15 PSS risk prediction models were included. The incidence of early PSS (occurring ≤one week from stroke onset) and late PSS (occurring >one week from stroke onset) was 4.5% and 2.1%, respectively. Cortical involvement, functional deficits, increasing lesion size, early seizures, younger age, and haemorrhage were the commonest predictors across the models. SeLECT demonstrated greatest predictive accuracy (AUC 0.77 [95% CI: 0.71‐0.82]) for late PSS following ischaemic stroke, and CAVE for predicting late PSS following haemorrhagic stroke (AUC 0.81 [0.76‐0.86]). Fourteen of 15 studies demonstrated a 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 as useful research tools by identifying stroke patients at high risk of developing PSS for recruitment into studies of anti‐epileptic drug prophylaxis.