Hemorrhagic Transformation Following Childhood Cardioembolic Stroke Is Not Increased in Anticoagulated Patients

Objective: To characterize the risk of hemorrhagic transformation following cardioembolic stroke in childhood, and whether anticoagulation impacts that risk. Methods: Ninety-five children (1 month-18 years) with cardioembolic arterial ischemic stroke between January 1, 2009, and December 31, 2019, at 2 institutions were identified for retrospective chart review. Neuroimaging was reviewed to assess for hemorrhagic transformation. Results: There were 11 cases of hemorrhagic transformation; 8 occurred within 2 days of stroke diagnosis. Risk of hemorrhagic transformation did not differ in patients with and without anticoagulation use (15% vs 9%, estimated risk difference 5%; CI –9%, 19%). Stroke size did not predict hemorrhagic transformation (OR 1.004, 95% CI 0.997, 1.010). Risk of hemorrhagic transformation was higher in strokes that occurred in the inpatient compared with the outpatient setting (16% vs 6%). Conclusion: Hemorrhagic transformation occurred in 11% of pediatric cardioembolic ischemic stroke, usually within 2 days of stroke diagnosis, and was not associated with anticoagulation or stroke size.

Atrial Fibrillation and Stroke Symptoms in the REGARDS Study

Background It is unknown if stroke symptoms in the absence of a stroke diagnosis are a sign of subtle cardioembolic phenomena. The objective of this study was to examine associations between atrial fibrillation (AF) and stroke symptoms among adults with no clinical history of stroke or transient ischemic attack (TIA). Methods and Results We evaluated associations between AF and self‐reported stroke symptoms in the national, prospective REGARDS (Reasons for Geographic and Racial Differences in Stroke) cohort. We conducted cross‐sectional (n=27 135) and longitudinal (n=21 932) analyses over 8 years of follow‐up of REGARDS participants without stroke/transient ischemic attack and stratified by anticoagulant or antiplatelet agent use. The mean age was 64.4 (SD±9.4) years, 55.3% were women, and 40.8% were Black participants; 28.6% of participants with AF reported stroke symptoms. In the cross‐sectional analysis, comparing participants with and without AF, the risk of stroke symptoms was elevated for adults with AF taking neither anticoagulants nor antiplatelet agents (odds ratio [OR], 2.22; 95% CI, 1.89–2.59) or antiplatelet agents only (OR, 1.92; 95% CI, 1.61–2.29) but not for adults with AF taking anticoagulants (OR, 1.08; 95% CI, 0.71–1.65). In the longitudinal analysis, the risk of stroke symptoms was also elevated for adults with AF taking neither anticoagulants nor antiplatelet agents (hazard ratio [HR], 1.41; 95% CI, 1.21–1.66) or antiplatelet agents only (HR, 1.23; 95% CI, 1.04–1.46) but not for adults with AF taking anticoagulants (HR, 0.86; 95% CI, 0.62–1.18). Conclusions Stroke symptoms in the absence of a stroke diagnosis may represent subclinical cardioembolic phenomena or “whispering strokes.” Future studies examining the benefit of stroke symptom screening may be warranted.

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

Magnetic resonance imaging in the assessment of acute stroke

Introduction: Stroke is the second leading underlying cause of death globally and the leading cause of disability in adults. Stroke diagnosis should be performed quickly and efficiently to eliminate other potential causes of neurological deficits and to assess the time since the onset of clinical symptoms. Computed tomography (CT) and magnetic resonance imaging (MRI) are essential methods of detecting and evaluating stroke type and treatmentoptions. Diffusion and perfusion MR imaging is recommended for early stroke diagnosis, as well as for the selection of patients for recanalization therapy, and is considered effective in assessing treatment outcomes. The objectives of this study were to demonstrate the diagnostic value of diffusion and perfusion imaging in the diagnosis of acute ischemic stroke, analyze the role of magnetic resonance imaging in the selection of patients with acute stroke for recanalization therapy, and assess the effect of acute stroke complicity.Material and methods: The research is designed as a systematic review of the primary scientific research literature, which was published in English in relevant scientific databases (PubMed, Google Scholar, Medline) from 2014 to 2021.Results: 14 scientific research papers were singled out and the general characteristics of the study were analyzed (country, authors, year of publication, title of the study, type of study, study objectives, research methods, results and conclusion). A quality assessment of the included studies with cohort design and randomized controlled studies was performed, and most belong to the category of high-quality studies with a smaller number of medium-quality studies. The overall percentage of detected AIS cases in isolated studies using the DWI and/or PWI sequence was 90.8%. At the same time, the outcome of recanalization therapy was assessed using MRI studies (the number of patients who developed adverse events with functional data outcome 30 or 90 days after the procedure was observed). Comparison of MRI and CT imaging protocols provided data on the total percentage of detected acute stroke cases using CT imaging protocols (68.9%) and MRI imaging protocols (88.5%), which is why MRI is considered a superior method.Conclusion: Although CT is a suitable method for visualizing bleeding and also for early differentiation of hemorrhagic from ischemic stroke, if MRI imaging is available, it is recommended to use DWI, PWI, MRA sequences for a more accurate diagnosis of stroke in the acute phase.

Dementia Risk Following Ischemic Stroke: A Systematic Review and Meta-Analysis of Factors Collected at Time of Stroke Diagnosis

Objective: To assess incidence rates and predictors of dementia after ischemic stroke. Methods: A search was conducted on Embase and Medline for reports published up to November 2019. Studies were included if they: 1) assessed dementia incidence among patients with ischemic stroke diagnosis and 2) excluded patients with prevalent dementia at baseline. The main analysis included: 1) absolute risk; 2) incidence rates (per 100 person-years) and 3) patient-level predictors (demographics, CVD history and major cardiac events, previous stroke and TIA, stroke location, disability post-stroke, chronic brain change and stroke mechanism). Additional predictors assessed included study setting (clinic or registry), method of dementia diagnosis (Diagnostic and Statistical Manual of Mental Disorders (DSM), National Institute of Neurological Disorders and Stroke (NINDS) or both) and inclusion of patients with recurrent or first-ever stroke. A random effects meta-analysis was undertaken. Risk of bias in included studies was assessed in terms of selection, comparability and outcome. Results: 4,325 studies were screened in the title and abstract phase after removing duplicates and 280 eligible studies were screened for full text. A total of 21 studies met the inclusion criteria and were included in the meta-analysis, representing 55,183 patients with ischemic stroke, with average age of 70 years (range 65-80 years) and average follow-up of 29 months. The majority of included studies were conducted in a hospital setting (n=17/21). The overall rate of dementia after ischemic stroke was 13.0 per 1000 person-years (95% CI 6.0, 36.0). Incidence rates were eight times higher in hospital-based studies (17.0, 95% CI 8.0, 36.0) compared to registry-based studies (1.8, 95% CI 0.8, 4.0). Absolute dementia risk after stroke was 20% at 5 year, 30% at 15 years and 48% at 25 years of follow-up. Incidence rates were 1.5 times higher in studies that included patients with recurrent ischemic stroke compared to estimates from studies that included first-time ever stroke patients only. There was 33% difference in dementia incidence in the later study periods (2007-2009) compared to (1996-2006). Statistically significant predictors of dementia after ischemic stroke included female gender (OR=1.2, 95% CI 1.1, 1.4), hypertension (1.4, 95% 1.1, 2.0), diabetes mellitus (1.6, 95% 1.3, 2.1), atrial fibrillation (1.9, 95% 1.2, 3.0), previous stroke (2.0, 95% CI 1.6, 2.6), presence of stroke lesion in dominant hemisphere (2.4, 95% 1.3, 4.5), brain stem/cerebellum (0.5, 95% CI 0.3, 0.9) or frontal lobe (3.7, 95% CI 1.2, 12.0), presence of aphasia (7.9, 95% CI 2.4, 26.0), dysphasia (5.8, 95% CI 3.0, 11.3), gait impairment (1.7, 95% CI 1.1, 2.7), presence of white matter hyperintensities (3.2, 95% CI 2.0, 5.3), medial temporal lobe atrophy (3.9, 95% CI 1.9, 8.3) and transient ischemic attack (TIA) as the predisposing aetiology for ischemic stroke (0.44, 95% CI 0.22, 0.88). Conclusion: Factors routinely collected at time of admission guide informed monitoring of patients at highest risk of progression to dementia after acute ischemic stroke. Predictors of dementia after acute ischemic stroke should be assessed as distinct features from those established for general dementia.