Introduction.: Stroke is a significant underlying cause of epilepsy. Seizures due to ischemic stroke (IS) are generally categorized into early seizures (ESs) and late seizures (LSs). Seizures in thrombolysis situations may raise the possibility of other etiology than IS.Aim.: We overtook a systematic review focusing on the pathogenesis, prevalence, risk factors, detection, management, and clinical outcome of ESs in IS and in stroke/thrombolysis situations. We also collected articles focusing on the association of recombinant tissue-type plasminogen activator (rt-PA) treatment and epileptic seizures.Results.: We have identified 37 studies with 36,775 participants. ES rate was 3.8% overall in patients with IS with geographical differences. Cortical involvement, severe stroke, hemorrhagic transformation, age (<65 years), large lesion, and atrial fibrillation were the most important risk factors. Sixty-one percent of ESs were partial and 39% were general. Status epilepticus (SE) occurred in 16.3%. 73.6% had an onset within 24 h and 40% may present at the onset of stroke syndrome. Based on EEG findings seizure-like activity could be detected only in approximately 18% of ES patients. MRI diffusion-weighted imaging and multimodal brain imaging may help in the differentiation of ischemia vs. seizure. There are no specific recommendations with regard to the treatment of ES.Conclusion.: ESs are rare complications of acute stroke with substantial burden. A significant proportion can be presented at the onset of stroke requiring an extensive diagnostic workup.

Background: Neuroinflammation plays an important role in the pathogenesis of acute ischemic stroke (AIS) and peripheral leukocyte counts have proved to be independent predictors of stroke severity and outcomes. Clinical significance of large vessel occlusion (LVO) in AIS is increasing, as these patients are potential candidates for endovascular thrombectomy and likely to have worse outcomes if not treated urgently. The aim of our study was to assess the relationship between on admission leukocyte counts and the presence of LVO in the early phase of AIS. Methods: We have conducted a cross-sectional, observational study based on a registry of consecutive AIS patients admitted up to 4.5 h after stroke onset. Blood samples were taken at admission and leukocyte counts were measured immediately. The presence of LVO was verified based on the computed tomography angiography scan on admission. Results: Total white blood cell (WBC) and neutrophil counts were significantly higher in patients with LVO than those without LVO (P < 0.001 respectively). After adjustment for potential confounders total WBC counts (adjusted OR: 1.405 per 1 × 10⁹/L increase, 95% CI: 1.209 to 1.632) and neutrophil counts (adjusted OR: 1.344 per 1 × 10⁹/L increase, 95% CI: 1.155 to 1.564) were found to have the strongest associations with the presence of LVO. Total WBC and neutrophil counts had moderate ability to discriminate an LVO in AIS (AUC: 0.667 and 0.655 respectively). No differences were recorded in leukocyte counts according to the size of the occluded vessel and the status of collateral circulation in the anterior vascular territory. However, total WBC and neutrophil counts tended to be higher in patients with LVO in the posterior circulation (p = 0.005 and 0.010 respectively). Conclusion: Higher admission total WBC and neutrophil counts are strongly associated with the presence of LVO and has moderate ability to discriminate an LVO in AIS. Detailed evaluation of stroke-evoked inflammatory mechanisms and changes according to the presence of LVO demands further investigation.

Background/Objectives: Current guidelines recommend intravenous thrombolysis (IVT) followed by mechanical thrombectomy (MT) for patients with acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). This combined approach, known as bridging therapy (BT), is believed to increase the likelihood of a favorable functional outcome when administered within 4.5 h of symptom onset. However, the benefits of BT over direct mechanical thrombectomy (d-MT) remain debated. This study aimed to compare the outcomes of AIS-LVO patients undergoing MT within 6 h of symptom onset, with and without prior IVT. Methods: Within the prospective Transzlációs Idegtudományi Nemzeti Laboratórium (TINL) STROKE-registry, AIS-LVO patients admitted to the Department of Neurology, University of Pécs between February 2023 and June 2024 were investigated. The primary endpoint was the proportion of patients reaching functional independence at 90 days, defined as a modified Rankin Scale (mRS) score of 0–2. Secondary endpoints included clinical improvement at 72 h (National Institute of Health Stroke Scale [NIHSS] score of ≤1 or a change from baseline [ΔNIHSS] of ≥4) and successful recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] score ≥ 2). Safety outcomes were evaluated based on thrombus migration and intracranial hemorrhage (ICH). Results were compared using linear and logistic regression analyses adjusted for baseline variables. Results: Of 82 patients, 51 (62.2%) received BT, while 31 (37.8%) underwent d-MT. The BT group showed a significantly higher rate of functional independence (45.7% vs. 17.2%, p = 0.014) and a lower 90-day mortality rate (13.7% vs. 35.5%, p = 0.029). Multivariate analysis revealed that IVT was independently associated with favorable functional outcomes (p = 0.011) and reduced mortality (p = 0.021). No significant differences were observed in terms of clinical improvement at 72 h, successful recanalization, thrombus migration, or hemorrhagic transformation between the groups. Conclusions: This study supports current guidelines recommending BT for thrombectomy-eligible AIS-LVO patients, offering new insights into the ongoing clinical debate.

It was previously reported that diabetes mellitus (DM) and admission hyperglycemia (aHG) were associated with poor clinical outcomes in patients with acute ischemic stroke (AIS) who were treated with intravenous thrombolysis (IVT) or mechanical thrombectomy (MT). Our study aimed to assess the prognostic effect of DM and aHG (≥7.8 mmol/L) on clinical outcomes in patients treated with recanalization therapies (IVT and MT). Our multicentric study was based on data from the prospective STAY ALIVE stroke registry between November 2017 and January 2020. We compared the demographic data, clinical parameters and time metrics between recanalized DM and non-DM groups, and we analyzed the impact of DM and aHG on 90-day functional outcome, 90-day mortality, symptomatic intracranial hemorrhage (sICH), and successful recanalization. Statistical analyses were also performed in two subgroups: (1) patients treated with IVT alone and (2) patients treated with MT. Altogether, we included 695 patients from the three participating stroke centers in Hungary. Regarding the overall population, patients with diabetes were older (72 vs. 67 years, p < 0.001) and comorbidities were more frequent. There were significant differences in the 90-day good functional outcome (48.9% vs. 66.7%, p < 0.001), 90-day mortality (21.9% vs. 11.6%, p < 0.001) and the rate of symptomatic intracranial hemorrhaging (sICH) (7.8% vs. 2.2%, p < 0.001) between the groups. Diabetes and aHG were independently associated with a poor clinical outcome (OR 2.02, 95% CI 1.31–3.11, p = 0.001; OR 2.09, 95% CI 1.39–3.14, p < 0.001) and mortality at 3 months (OR 2.45, 95% CI 1.35–4.47, p = 0.003; OR 2.42, 95% CI 1.37–4.28, p = 0.002) and sICH (OR 4.32, 95% CI 1.54–12.09, p = 0.005; OR 4.61, 95% CI 1.58–13.39, p = 0.005) in the overall population. However, the presence of DM and aHG was not correlated with successful reperfusion (OR 0.39, 95% CI 0.09–1.67, p = 0.205; OR 0.42, 95% CI 0.09–1.97, p = 0.274) after MT. Our study revealed that diabetes and hyperglycemia on admission were correlated with poor clinical outcomes at 3 months in patients with acute stroke regardless of the recanalization method. In addition, the variables were also associated with sICH after recanalization therapies. However, successful recanalization was not associated with DM and aHG in patients who underwent MT.

The early detection of large-vessel occlusion (LVO) strokes is increasingly important as these patients are potential candidates for endovascular therapy, the availability of which is limited. Prehospital LVO detection scales mainly contain symptom variables only; however, recent studies revealed that other types of variables could be useful as well. Our aim was to comprehensively assess the predictive ability of several clinical variables for LVO prediction and to develop an optimal combination of them using machine learning tools. We have retrospectively analysed data from a prospectively collected multi-centre stroke registry. Data on 41 variables were collected and divided into four groups (baseline vital parameters/demographic data, medical history, laboratory values, and symptoms). Following the univariate analysis, the LASSO method was used for feature selection to select an optimal combination of variables, and various machine learning methods (random forest (RF), logistic regression (LR), elastic net method (ENM), and simple neural network (SNN)) were applied to optimize the performance of the model. A total of 526 patients were included. Several neurological symptoms were more common and more severe in the group of LVO patients. Atrial fibrillation (AF) was more common, and serum white blood cell (WBC) counts were higher in the LVO group, while systolic blood pressure (SBP) was lower among LVO patients. Using the LASSO method, nine variables were selected for modelling (six symptom variables, AF, chronic heart failure, and WBC count). When applying machine learning methods and 10-fold cross validation using the selected variables, all models proved to have an AUC between 0.736 (RF) and 0.775 (LR), similar to the performance of National Institutes of Health Stroke Scale (AUC: 0.790). Our study highlights that, although certain neurological symptoms have the best ability to predict an LVO, other variables (such as AF and CHF in medical history and white blood cell counts) should also be included in multivariate models to optimize their efficiency.