Introduction

Globally, stroke accounted for 11.6% of all deaths in 2019, making it the second leading cause of death.1 Despite significant advancement in stroke management over the last decade, survivors continue to experience disabilities and lasting complications. The burden of post-stroke psychological issues is a significant concern and places unnecessary strain on healthcare resources. Post‐stroke seizure is defined as single or multiple convulsive episodes after stroke and thought to be related to reversible or irreversible cerebral damage due to stroke regardless of the time of onset following the stroke.2 Post-stroke epilepsy refers to recurrent seizures following stroke with a confirmed diagnosis of epilepsy.2 Early seizures (acute symptomatic seizures) occur within seven days following an infarct, whereas late seizures (remote symptomatic seizures) occur beyond a week.3 One previous study conducted in the US reported that ischemic and haemorrhagic stroke accounted for 11% of epilepsy cases.4

The prevalence of PSS varies from 4% to 43%, with the highest rates following cortical and large haemorrhagic infarcts.5 The incidence increases from 1.5% at three months to 9% at five years post-stroke.6 PSS undoubtedly negatively impact a patient’s prognosis, with prolonged hospitalisations, rising disability rates, poorer quality of life, and increased hospital expenses, leading to significantly higher mortality and severe disability in stroke patients.7,8 The type of seizure observed may vary from focal to bilateral convulsive seizures,9 generalized seizures10 and status epilepticus.11

Significant variations exist in stroke frequency, fatality, and incidence across populations.12 In Asia, a higher prevalence of lacunar stroke and intracerebral haemorrhage in men is observed, with South Asians facing a 1.5 to 2-fold higher risk compared to Europeans.13,14 Apart from stroke subtype, size, location, severity of the vascular lesion, and early seizures, the reported findings on seizure predictors may be variable.15 Other independent predictors identified for PSS were young age, larger lesions, and haemorrhagic lesions6 The risk of post-stroke epilepsy was higher in patients with hemorrhagic stroke compared to those with ischemic stroke in a Taiwanese study.16

Despite existing studies, limited research addresses PSS risk factors locally. This study aims to fill this gap by analyzing sociodemographic, clinical and stroke-related variables in patients at a local tertiary hospital.

Methodology Study Design

This prospective case–control study was conducted between March 2023 and October 2023 in Hospital Canselor Tuanku Muhriz, the National University of Malaysia, with the local Ethics and Research Board approval (FF-2023-098). A case–control study was conducted to investigate the risk factors associated with post-stroke seizures, which are relatively unknown among the Malaysian population. As a result, targeted risk factors are currently unaddressed and need to be mitigated. Funding was obtained from the National University of Malaysia. All patients admitted with the diagnosis of stroke with seizures were recruited via purposive sampling. The control group consists of patients who had been diagnosed with stroke but without seizures. The participants in the case and control groups were closely matched to minimize bias.

The patients are diagnosed with acute ischemic stroke on presentation based on their clinical history, examination and radiographic evidence of cerebral infarct in imaging. Those presenting with debilitating neurological diseases, stroke mimics (underlying seizures, epilepsy, hyperglycemia, metabolic causes, infection, and cerebral venous sinus thrombosis), prior history of seizure, traumatic brain injury, previous neurosurgery, and cerebral tumours were excluded from this study.

The following clinical information was gathered using a data collection sheet during patient recruitment: sociodemographic details, relevant clinical data, including the National Institute of Health Stroke Scale (NIHSS) score at presentation, classification of stroke based on Trial of Org 10172 in Acute Stroke Treatment (TOAST) (large-artery atherosclerosis, cardioembolism, small-vessel occlusion, stroke of other determined etiology, and stroke of undetermined etiology), modified Rankin Scale (mRS) score at discharge, the timing of seizure after stroke onset, type of seizure, and frequency of seizure.

An EEG was performed for all participants inpatient within 72 hours of admission, and recorded on the Nicolet One Extension (V32 Amplifier) using 24 reusable gold electrodes affixed to the scalp according to the international 10–20 system. The abbreviations on the EEG are as follows: Fp – frontopolar, C – central, F – frontal, T – temporal, P – parietal, and O – occipital. Bipolar longitudinal and average referential montages were utilized for evaluation. Each recording was approximately half an hour with the filter configuration as follows: 50 Hz filter; low-frequency filter: 0.5 Hz; high-frequency filter: 70 Hz.

Two trained neurologists blinded to the clinical and radiological findings evaluated the EEG. Each gave an individual report of the EEG based on the findings. Upon comparison of individual reports, any discrepancies are discussed, with the EEG recording re-examined before producing a final joint report for classification. An abnormal EEG was defined as generalized slowing (GS), focal slowing (FS), or the presence of epileptiform patterns (spikes, sharp waves, rhythmic and periodic patterns). Spikes were defined as sharp-pointed peaks lasting 20–70ms and amplitude more than 50 microV. Sharp waves were sharp-pointed peaks lasting 70–200 ms and amplitude of 100–200 microV. Periodicity refers to a series of spike or sharp wave complexes occurring at regular intervals. Generalized slowing was defined when the dominant rhythm is within the theta (4–8 Hz) or delta (< 4 Hz) frequency bands, occurring over all regions of the head. Focal slowing (FS) was defined as slow activity (theta or delta) occurring at a limited region of the head.

Statistical Analysis

We used the IBM SPSS Version 26 statistical program for data entry and analysis. Ninety-five percent confidence intervals were reported where applicable, and the significance threshold was pre-set at p-value <0.05. Normality testing for continuous variables was performed using the ratio of skewness, standard error and kurtosis as the sample size was more than 100. Categorical variables [eg gender, race, comorbidities, blood pressure classification, causes of stroke, classification of stroke and seizure, location of stroke, type of stroke treatment, NIHSS and mRS] and not normally distributed continuous variables were reported as frequencies and percentages. Data transformation was not performed as comparison of risk factors were of categorical variables. Thus, the mean and standard deviation (SD) were used to define normally distributed continuous variables [eg age, systolic and diastolic blood pressure measurements]. A non-parametric comparative test was used as the variables were categorical with a dichotomous outcome. Thus, the non-parametric Mann–Whitney U-test was applied to compare the qualitative variables. Lastly, the Odds Ratio was used to analyze the risk of post-stroke seizure across variables.

Results Sociodemographic Characteristics

A total of 292 patients admitted with ischaemic stroke were screened for this study. Eventually, 180 patients were recruited based on the inclusion and exclusion criteria stated and divided equally between cases and controls, as shown in Table 1. The participants were between 33 and 91 years of age, with a mean age of 64.42 years (SD ±13.4) for the control group and 66.96 years (SD ±1.31) for the cases. As part of our analysis, we performed normality testing on all continuous variables, including age, systolic and diastolic blood pressure, NIHSS score, and mRS on discharge. The skewness ratio, standard error and kurtosis were used for normality testing as the sample size was more than fifty. Therefore, medium-sized samples’ absolute z-scores for either skewness or kurtosis that were larger than 3.29 indicated a non-normal distribution. The median NIHSS score within our cohort was 6, with an interquartile range of 10. From our investigation, age and blood pressure readings demonstrated a normal distribution pattern amongst stroke with and without seizure cohorts. The present study identified a significantly higher prevalence of concomitant dyslipidemia and small vessel occlusion among case participants as compared to their respective controls. The other parameters were almost identical between the control and experimental groups.

Table 1 Sociodemographic Characteristics of Study Participants

One hundred and seven participants demonstrated EEG abnormalities, with 63 participants in the cases group and 44 in the control group, as shown in Table 2. Abnormal EEGs were seen in higher proportion among case groups in those with dyslipidemia, small vessel occlusion and lacunar type stroke. The majority of abnormalities detected were focal changes, with a higher proportion of participants with epileptiform changes in the cases group.

Table 2 Sociodemographic Characteristics of Study Participants with Abnormal EEG

Associations Between Post-Stroke Seizure and its Risk Factors

A non-parametric test was utilized to investigate the relationship between sociodemographic factors, underlying comorbidities, stroke and seizure characteristics, treatment, potential complications, and PSS among study participants. The Mann–Whitney U-test was used to analyze the above-mentioned risk factors, with the outcome being post-stroke seizure in the case group. The Mann–Whitney U-test results revealed that several factors including gender (U = 3375, z = −2.241, p =0.013), race (U = 3451, z = −1.877, p=0.015), dyslipidemia (U = 2835, z = −4.026, p <0.001), previous history of stroke (U =3510, z = −1.863, p <0.031), large artery atherosclerosis (U = 2835, z = −4.128, p <0.001), and small vessel occlusion (U = 2610, z = −4.800, p <0.001), were significantly associated with the occurrence of seizure following a stroke as shown in Table 3. Additionally, blood pressure on presentation (U = 3555, z = −1.917, p <0.028) and thrombolysis treatment for stroke (U = 3645, z = −2.177, p <0.029) were also significantly associated with the occurrence of post-stroke seizure.

Table 3 Associations Between Risk Factors and Post-Stroke Seizures

Risk of Post-Stroke Seizures

The risk of developing a seizure following a stroke event was examined using Odds Ratio (OR). The contingency table was examined and the odds ratio was calculated. Adjustments for potential confounders were not made as factors associated with increased post-stroke seizure are relatively unknown. In this study, the male gender had a statistically significant increased odds of post-stroke seizure at 1.974 (CI 1.088–3.583), as depicted in Table 4. Besides that, concurrent comorbid conditions, including hypertension, diabetes mellitus, atrial fibrillation, ischemic heart disease, and past stroke, had been shown to have elevated odds of post-stroke seizure but were not statistically significant. Dyslipidemia had a significantly elevated risk of post-stroke seizure outcome at OR 3.480 (CI 1.880–6.439) and is statistically significant. Small vessel occlusions were also associated with an elevated risk at OR 4.578 (CI 2.424–8.643). Conversely, the study found that participants with high blood pressure on presentation, large artery atherosclerosis and those that underwent thrombolysis showed decreased odds of post-stroke seizure. Notably, the results indicated a statistically significant increase in the risk of post-stroke seizure in individuals with epileptiform changes on their EEG, with an odds ratio of 3.630 (CI 1.136–11.603).

Table 4 Odds for Post-Stroke Seizure Based on Each Risk Factor

Discussion

Our findings demonstrated an association between gender, race, dyslipidemia, history of stroke, large artery atherosclerosis, small vessel occlusions, blood pressure on presentation, and thrombolysis administration with PSS. The odds of PSS were elevated among males, those with dyslipidemia, small vessel occlusions, and in participants with epileptiform changes on EEG. Lower odds of PSS were seen in those with high blood pressure on presentation, large artery atherosclerosis, and those given thrombolysis treatment.

Sociodemographic Factors

The present study involved a cohort comprising 180 participants, with an equal distribution of individuals between the control and case groups. Notably, the elderly population was more prevalent in the study, as evidenced by a mean age range of 64.42 to 66.96. This is not unusual, as the prevalence of stroke in the elderly is significantly higher as published in the World Stroke Organization 2022 global fact sheet, where over two-thirds of cases occur beyond 50 years old.17 The number of comorbidities observed increases with age, particularly beyond 65 years old and may contribute to the risk of stroke.18

Post-stroke seizure events are significantly associated with sociodemographic factors, especially gender and race, with a p-value of less than 0.05. Our study found that the male gender is 1.974 times more likely to develop PSS, which is comparable to other similar studies reporting a predominance of PSS among males.19–22 However, other studies demonstrated conflicting results, with higher incidence of early PSS among women in studies from Saudi Arabia and Canada.23,24 Besides that, a meta-analysis by Ma et al suggested there was no association between gender and epileptic seizures.25 Conflicting results in these studies may be due to coagulation status, sex hormones, genetic backgrounds, social interactions, and lifestyle choices.26

From our analysis, males of Chinese descent were at particular risk for PSS. In Singapore, the highest incidence of stroke was observed among Chinese men, although a lower than expected response rate confounded the study.27 There are existing studies pointing to the high prevalence of atherosclerosis and carotid plaques among Chinese men of advancing age.28 This is possibly linked to an elevated presence of dyslipidemia with high triglyceride and low-density lipoprotein among the Chinese population.29,30 All these factors may contribute to an increased rate of post-stroke seizure among the population, given its links to dyslipidemia. It is important to note that our participants’ mean age is above 65 years old. Therefore, this may not reflect the exponential rise in the rate of obesity and metabolic disorders among younger populations in this region due to the shift in food consumption trends over the past three decades.31 Studies focusing on stroke among young adults in the near future would reveal more data that would support the hypothesis that gender and race may be less significant than comorbidities in years to come.

Clinical Factors

In both our cohorts, most comorbidities observed were nearly equivalent, except for dyslipidemia. As mentioned earlier, dyslipidemia is a prominent risk factor and is significantly associated with PSS, with 3.48 times higher odds of developing PSS in our study. Existing studies also suggest an increased risk of PSS in stroke patients with underlying hypertension, dyslipidemia, or diabetes.32 Cleary et al reported that total serum cholesterol and other vascular risk factors, such as myocardial infarction, peripheral vascular disease, hypertension, and left ventricular hypertrophy, are related to late-onset epilepsy.33 The use of statins reduced the risk of progression to PSS in several studies, supporting the role of lower serum cholesterol in reducing PSS risk.34,35 This is especially important in this region where rates of dyslipidemia are relatively high.36

Blood pressure on presentation was another significant factor for PSS. Our study suggests that the case group had lower or normal median blood pressure on presentation than the control group. Hundozi et al have reported similar findings and this may be attributed to the protective effect of arterial hypertension against early epileptic seizures after ischemic stroke stems from improvement in brain perfusion and the reduction in the size of the ischemic area.37 Some studies did suggest that hypertension is not an independent risk factor for PSS, and another reported a higher incidence of PSS with hypertension.25,32 Therefore, we may not fully understand the mechanism of how blood pressure relates to PSS, and it might be detrimental to maintain higher blood pressure levels that may increase the risk of haemorrhagic transformation in ischemic strokes which invariably predisposes to early epileptic seizures.38

Stroke due to small vessel occlusion was significantly associated with PSS with an increase in risk by 4.57 folds. Small vessel diseases are known to cause deep infarcts, albeit small, as seen in imaging as leukoaraiosis, although its role in PSS is unclear.39 Coupled with hypertension, it is identified as an independent risk factor for seizures in advancing age in a few studies.40,41 This may be attributed to the upregulation of the receptor angiotensin II type I in the hippocampus of patients with mesial temporal sclerosis, suggesting an independent role of the renin–angiotensin system in temporal lobe epilepsy.42,43 On the other hand, Ferreira‐Atuesta et al reported that small vessel occlusion only carries a lower risk of acute symptomatic seizures post-stroke, approximately 6%.44

In our cohort, participants who had thrombolytic therapy had a reduced risk of post-stroke seizure with an odds ratio of 0.31. Several studies have reported that patients who had thrombolysis for ischaemic stroke did not show an associated risk for post-stroke seizure or epilepsy.44–47 Reperfusion therapies may aid in reducing the subsequent size of the infarct by rescuing the tissue within the ischaemic penumbra, which is potentially pro-epileptogenic.46 On the contrary, reperfusion has been associated with seizures in some studies, especially when complicated by the presence of microbleeds or secondary haemorrhages. However, these studies have several confounders, including the severity of the patients requiring reperfusion treatment who generally have a higher NIHSS score and larger lesion area on presentation.44,46 The overall benefit seen is likely achieved by carefully selecting patients for thrombolysis.

Interestingly, atrial fibrillation, a known risk factor for cardioembolic stroke, was not associated with PSS in our study. Studies have identified it as a risk factor for late-onset and early-onset PSS due to its association with cortical damage.19,48–50 Our findings were supported by studies from Taiwan and Lithuania, demonstrating that atrial fibrillation was not associated with an increased risk for PSS.34,51 Beghi et al also reported that there was no significant association between cardioembolic stroke and acute symptomatic seizures.15 It is imperative to thoroughly examine the various factors influencing atrial fibrillation in PSS that will contribute to its management.

The prevalence of abnormal EEG in this study is relatively high, with 107 participants with documented abnormal EEG from both cases and controls. Dyslipidemia was the most prevalent comorbidity with regard to EEG abnormalities. While large artery atherosclerosis has been identified as a common observation in patients with PSS, the same was not demonstrated in the present study. However, the small sample size limits the generalizability of this finding. In addition, the stroke localization was similar between both cohorts except for stroke involving the lacunar territories, which was more common among the case group. These findings add to the growing body of literature on the comorbidity and pathophysiology of seizures and stroke.

The electroencephalogram (EEG) aids in detection and prognostication in acute stroke by detecting seizures and specific stroke patterns, such as lateralized periodic discharges, which are associated with early seizures.52–55 We observe a relatively higher proportion of patients with PSS demonstrating EEG abnormalities. As expected, those with the presence of epileptiform discharges were more likely to develop seizures, 3.63 times higher. Our data was concordant with a prospective study that epileptiform discharge in early post-stroke EEG is an independent predictor of epilepsy one year after stroke.56 Specific EEG patterns such as periodic lateralized epileptic discharges are found to be closely related to early seizures.55 Interictal epileptiform discharges have been documented as a predictive biomarker for seizure recurrence in post-stroke epilepsy.57 EEG background asymmetry and focal epileptiform discharges have a greater seizure recurrence risk in post-stroke patients.58 The cerebral hemodynamic changes from PSS result from electrophysiological instability, neurotransmitter imbalance, disruption of neuronal network, and post-stroke glial proliferation and activation.7

Based on our study, the at-risk population should be screened and risk stratified at an earlier age to initiate treatment early as necessary. Additionally, performing imaging techniques such as CT angiography of the brain to identify large and small vessel occlusions would be beneficial for prognostication. Expanding thrombolysis services to more hospitals is likely to improve overall stroke management outcomes and reduce the risk of post-stroke seizures (PSS). Future studies should investigate the risk of PSS in patients with different stroke subtypes, such as hemorrhagic stroke.

Limitations

Due to limited resources, we did not perform continuous EEG monitoring for our participants. Doing so would increase the detection rate of abnormalities in the EEG during hospitalization. In addition, as a single-centred study, these findings may not represent the overall population. Therefore, large-scale prospective studies need to be conducted to enhance our understanding of post-stroke seizure risks and their impact on individuals and society. Besides that, seizures occurring following discharge from the hospital may not be picked up, particularly, those who present with focal seizures as it is a prospective case–control study. Purposive sampling was utilized due to its flexibility to facilitate recruitment of case and controls. However, we are aware that this may be prone to potential bias.

Conclusion

Post-stroke seizures (PSS) can lead to poorer functional outcomes and extended hospitalization. This study highlights the importance of using EEGs to identify PSS and recognizing at-risk groups, which include males of Chinese descent in Asia, individuals with dyslipidemia, those with small vessel occlusions, and patients presenting with low to normal blood pressure. Recognizing these factors is crucial for developing comprehensive and effective management plans. Our findings contribute to the understanding of PSS risk factors and underscore the need for targeted interventions. Future research should focus on longitudinal studies to further elucidate the pathogenesis of PSS and explore the effectiveness of tailored management strategies in improving patient outcomes.

Data Sharing Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Ethical Publication Statements

We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. The study complies with the Declaration of Helsinki. The ethics committee approval was obtained for this study.

Patient Consent Statement

Patients were informed regarding the purpose of the study and consent was obtained.

Acknowledgments

We thank the Dean of the Faculty of Medicine at the National University of Malaysia for allowing us to publish this manuscript.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was funded by the National University of Malaysia following approval from the local ethics and research board with the grant code: FF-2023-098.

Disclosure

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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