Risk models to predict late-onset seizures after stroke: A systematic review

Abstract

Background and purpose: We performed a systematic review to evaluate available risk models to predict late seizure onset among stroke survivors.

Methods: We searched major databases (PubMed, SCOPUS, and Cochrane Library) from inception to October 2020 for articles on the development and/or validation of risk models to predict late seizures after a stroke. The impact of models to predict late-onset seizures was also assessed. We included seven articles in the final analysis. For each of these studies, we evaluated the study design and scope of predictors analyzed to derive each model. We assessed the performance of the models during internal and external validation in terms of discrimination and calibration.

Results: Three studies focused on ischemic stroke alone, with c-statistic values ranging from 0.73 to 0.77. The SeLECT model from Switzerland was externally validated in Italian, German, and Austrian cohorts where c-statistics ranged from 0.69 to 0.81. This model along with the PSEiCARe model, were internally validated and calibration performance was provided for both models. The CAVS and CAVE models reported on the risk of late-onset seizures in patients with hemorrhagic stroke. The CAVS model derivation cohort was racially diverse. The CAVS model's c-statistic was 0.76, while the CAVE model had a c-statistic of 0.81. Calibration and internal validation were not performed for either study. The CAVS model, created from a Finnish population, was externally validated in American and French cohorts, with c-statistics of 0.73 and 0.69, respectively. Finally, the two studies focusing on both types of stroke came from the PoSERS and INPOSE models. Neither model provided c-statistics, calibration metrics, internal or external validation information. We found no evidence of the presence of impact studies to assess the effect of adopting late-onset seizure risk models after stroke on clinical outcomes.

Conclusion: The SeLECT model was the only model developed in line with proposed guidelines for appropriate model development. The model, which was externally validated in a very similar and homogeneous population, may need to be tested in a more racially/ethnic diverse and younger population; testing the SeLECT model, accounting for overall brain health is likely to improve the identification of high-risk patients for late post stroke seizures.

Keywords: Late-onset seizures; Prediction; Risk model; Stroke.