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Published on: June 21, 2019
Predictive factors for the development of epilepsy after ischemic stroke
Matías Alet1, Fernando Márquez2, Abril Marone3
1Centro Integral de Neurología Vascular, Fleni. Ciudad Autónoma de Buenos Aires, Argentina.
Objectives:
Ischemic stroke is one of the most common causes of epilepsy in adults. The incidence of post-stroke epilepsy (PSE) is approximately 7%. Risk factors are higher stroke severity, cortical localization, higher National Institute of Health Stroke Scale (NIHSS) upon admission and acute symptomatic seizures. We analyzed the predictive factors of PSE development in our population.
Materials And Methods:
Retrospective observational cohort of adult patients (age ≥ 18 years) with ischemic stroke assessed between January 2012 and June 2020. Patients with personal history of epilepsy and potentially epileptogenic structural injury other than acute or chronic stroke were excluded. Demographic, clinical and imaging variables were evaluated in a multivariate analysis for independent risk factors associated with PSE.
Results:
Medical records of 1586 stroke patients were reviewed, 691 met the inclusion criteria and had at least one year of follow-up. Of them, 428 (61.9%) were males. During follow-up, 6.2% had diagnosis of PSE (42/691) with a higher frequency of: previous ischemic stroke, higher NIHSS upon admission, treatment with rt-PA, higher Fazekas scale grade, cortical involvement, hemorrhagic transformation, acute symptomatic seizures, longer hospitalization and higher modified Rankin Scale (mRS) at discharge compared to the group without PSE. In a multivariate analysis, acute symptomatic seizures (OR=3.22, p: 0.033), cortical involvement (OR=0.274, p < 0.05), Fazekas scale score (OR=0.519, p < 0.05) and mRS at discharge (OR=1.33, p: 0.043) were independent risk factors.
Conclusions:
The variables related to higher risk of PSE were similar to those reported in the literature, highlighting the importance of neuroimaging findings, acute symptomatic seizures during hospitalization and neurological deficit at discharge. The data obtained will serve as the basis for construction of predictive models, allowing to individualize PSE probability in our population.
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