Early neurologic deterioration (END) is common and occurs within a few hours to days after an ischemic stroke. Traditionally, END has been treated as a collective entity, including the occurrence of new deficits (recurrence) and the aggravation of pre-existing neurologic deficits (progression). END arises from distinct mechanisms that require different therapeutic approaches. We reviewed clinical and experimental studies addressing the epidemiology, mechanisms, and treatment of END, focusing on differentiating END due to recurrence from END due to progression and on interventions including antiplatelet therapy, direct thrombin inhibition, and induced hypertension. Early recurrence is closely associated with thrombus growth and new ischemic events, particularly in atherothrombotic disease. Early recurrence is also common in patients with cancer-associated stroke. Thrombin and platelet activation play central roles under both conditions. In contrast, progression is mainly driven by infarct growth, that is, the evolution from incomplete infarction to complete infarction due to impaired perfusion, especially in lesions involving the subcortical fiber tracts. Therapeutic implications differ accordingly. Recurrence may respond to potent antithrombotic strategies, including combined antiplatelet and direct thrombin inhibition, whereas progression may benefit from induced hypertension. However, recurrence and progression often occur simultaneously, making clinical differentiation challenging. END should be conceptualized as a spectrum of clinical presentations arising from distinct mechanisms. Recognizing recurrence and progression as separate processes is essential for mechanism-oriented treatments. Future trials should adopt this framework to develop individualized strategies and improve outcomes in patients with acute stroke.

Background: and Purpose Although blood pressure (BP) elevation is common in acute ischemic stroke, and guidelines recommend reducing systolic BP to <185 mm Hg prior to reperfusion therapy, the safety and efficacy of active BP lowering in the hyperacute phase before endovascular thrombectomy (EVT) remain uncertain. Methods: We conducted a retrospective analysis of a prospective hospital-based registry that included consecutive patients with anterior circulation large-vessel occlusion who underwent EVT between 2016 and 2024. Patients were categorized into the active BP lowering in the emergency department (ED) group or the absence of BP lowering in the ED group based on whether they received intravenous antihypertensive treatment prior to EVT. The primary outcome was the distribution of the modified Rankin Scale (mRS) scores at 3 months. Propensity score matching and multivariable regression analyses were also performed. Results: Of the 492 included patients, 53 (10.8%) received active BP lowering in the ED. After propensity score matching, patients who underwent active BP lowering showed a worse distribution of 3-month mRS scores compared with those who did not receive BP lowering (adjusted odds ratio, 0.38; 95% confidence interval [CI], 0.18 to 0.80; p=0.013). The active BP lowering group exhibited greater infarct volume growth (adjusted β coefficient, 33.4; 95% CI, 18.2 to 48.7; p<0.001), whereas the incidence of symptomatic intracerebral hemorrhage did not differ between groups. Conclusions Active BP lowering in the ED before EVT was associated with worse functional outcomes and increased infarct growth without a corresponding reduction in the occurrence of symptomatic intracerebral hemorrhage. These findings highlight the need for caution in initiating antihypertensive therapy before reperfusion and support further investigations to define optimal pre-EVT BP management.

Background: and Purpose The relationship between thrombus histology and long-term stroke patient outcomes remains unexplored. We aimed to determine whether the histological characteristics of thrombi are associated with long-term outcomes in stroke patients and to identify the thrombus features linked to these outcomes. Methods: This retrospective multicenter cohort study included 512 patients with ischemic stroke who underwent endovascular thrombectomy between July 2017 and July 2023. Patients were followed up for long-term major adverse cardiovascular events occurrence. Thrombus histology was assessed using immunohistochemistry, including the proportion of fibrin, red blood cells, and platelets, as well as the distribution patterns categorized as layered, erythrocytic, diffuse platelet, and mixed. Results: During a median follow-up of 38.1 months, 164 patients experienced major adverse cardiovascular events, with an incidence rate of 3.02 per 100 person-years. Major adverse cardiovascular events occurrence was associated with the diffuse platelet pattern and proportion of platelets and red blood cells within the thrombus. After adjusting for confounders, the diffuse platelet pattern independently predicted major adverse cardiovascular events, including mortality and stroke recurrence. Subgroup analysis also demonstrated that the association between the diffuse platelet pattern and major adverse cardiovascular events was consistent across key clinical subgroups based on age (≥65 vs. <65 yr), atrial fibrillation, cancer status, and discharge medications. Conclusions Thrombus histology could provide predictive value for long-term prognosis. In particular, histological distribution patterns may be more important than simple composition in thrombus research, including in the prediction of prognosis.

Background: and Purpose Patients with ischemic stroke with both atrial fibrillation (AF) and large artery atherosclerosis (LAA) represent therapeutic challenges, and the optimal antithrombotic regimen remains uncertain. We conducted a meta-analysis comparing oral anticoagulant (OAC) monotherapy with OAC plus antiplatelet therapy in this population. Methods: PubMed and EMBASE were searched through June 30, 2025, for studies enrolling patients with ischemic stroke and evidence of both AF and LAA. Outcomes included recurrent ischemic stroke, major bleeding, all-cause mortality, and a composite outcome. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using the Peto method, with random-effects sensitivity analyses, stratified by short-term (<3 months) and long-term (≥1 year) follow-up. Results: Eight cohort studies were analyzed. In the short-term, combination therapy was associated with a reduced risk of recurrent ischemic stroke (OR, 0.37; 95% CI, 0.14–0.97; p=0.043), without a significant increase in major bleeding, although this association did not persist under random-effects sensitivity analysis (OR, 0.37; 95% CI, 0.13–1.07; p=0.067). Conversely, long-term combination therapy was associated with higher risks of major bleeding (OR, 1.25; 95% CI, 1.08–1.45; p=0.002), all-cause mortality (OR, 1.25; 95% CI, 1.01–1.54; p=0.039), and composite outcome (OR, 1.49; 95% CI, 1.27–1.74; p<0.001), without reducing recurrent ischemic stroke (OR, 1.12; 95% CI, 1.00–1.26; p=0.054). Conclusions While long-term OAC plus antiplatelet therapy increases bleeding risk without preventing recurrent stroke, short-term combination therapy may offer benefits in selected patients with concomitant LAA, though this early efficacy signal should be interpreted with caution.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 original studies on AI applications in ischemic stroke, categorized into outcome prediction, stroke risk prediction, diagnosis, etiology prediction, and complication and comorbidity prediction. Outcome prediction, the most explored category, includes studies predicting functional outcomes, mortality, and recurrence, often achieving high accuracy and outperforming traditional methods. Stroke risk prediction models effectively integrate clinical and imaging data, improving assessments of both first-time and recurrent stroke risks. Diagnostic tools, such as automated imaging analysis and lesion segmentation, streamline acute stroke workflows, while AI models for large vessel occlusion detection demonstrate clinical utility. Etiology prediction focuses on identifying causes such as atrial fibrillation or cancer-associated thrombi, using imaging and thrombus analysis. Complication and comorbidity prediction models address stroke-associated pneumonia and acute kidney injury, aiding in risk stratification and resource allocation. While significant advancements have been made, challenges such as limited validation, ethical considerations, and the need for better data collection persist. This review highlights the advancements in AI applications for addressing key challenges in stroke care, demonstrating its potential to enhance precision medicine and improve patient outcomes.

Microembolic signals (MESs), detected via transcranial Doppler ultrasonography, are essential biomarkers for assessing cerebrovascular risk, embolic events, and treatment outcomes. In this review, studies published between 2014 and 2024 were evaluated, specifically focusing on the clinical implications, associated conditions, and opportunities for advancements in MES monitoring technologies. A systematic PubMed search identified 327 articles, of which 60 were finally included in this review. MESs are associated with various conditions, including carotid/cerebral artery stenosis and atrial fibrillation. They predict adverse outcomes, including increased stroke risk, cognitive decline, and complications from procedures such as endovascular thrombectomy and unruptured aneurysm coiling. Furthermore, MESs serve as a surrogate marker for embolism, allowing for the evaluation of different procedural techniques to determine which approach minimizes embolic events. Advances in MES monitoring, including algorithms that distinguish gaseous and solid emboli and applications in pediatric cardiac surgery, have expanded its clinical utility. Moreover, emerging wearable and wireless technologies may expand the possibilities for MES monitoring.