Showing papers by "Luciano A. Sposato published in 2022"

Atrial Fibrillation Detected After Stroke and Transient Ischemic Attack: A Novel Clinical Concept Challenging Current Views

Abstract: Supplemental Digital Content is available in the text. Atrial fibrillation (AF) can be newly detected in approximately one-fourth of patients with ischemic stroke and transient ischemic attack without previously recognized AF. We present updated evidence supporting that AF detected after stroke or transient ischemic attack (AFDAS) may be a distinct clinical entity from AF known before stroke occurrence (known atrial fibrillation). Data suggest that AFDAS can arise from the interplay of cardiogenic and neurogenic forces. The embolic risk of AFDAS can be understood as a gradient defined by the prevalence of vascular comorbidities, the burden of AF, neurogenic autonomic changes, and the severity of atrial cardiopathy. The balance of existing data indicates that AFDAS has a lower prevalence of cardiovascular comorbidities, a lower degree of cardiac abnormalities than known atrial fibrillation, a high proportion (52%) of very brief (<30 seconds) AF paroxysms, and is more frequently associated with insular brain infarction. These distinctive features of AFDAS may explain its recently observed lower associated risk of stroke than known atrial fibrillation. We present an updated ad-hoc meta-analysis of randomized clinical trials in which the association between prolonged cardiac monitoring and reduced risk of ischemic stroke was nonsignificant (incidence rate ratio, 0.90 [95% CI, 0.71–1.15]). These findings highlight that larger and sufficiently powered randomized controlled trials of prolonged cardiac monitoring assessing the risk of stroke recurrence are needed. Meanwhile, we call for further research on AFDAS and stroke recurrence, and a tailored approach when using prolonged cardiac monitoring after ischemic stroke or transient ischemic attack, focusing on patients at higher risk of AFDAS and, more importantly, at higher risk of cardiac embolism.

Acute ischaemic stroke associated with SARS-CoV-2 infection in North America

Abstract: Background To analyse the clinical characteristics of COVID-19 with acute ischaemic stroke (AIS) and identify factors predicting functional outcome. Methods Multicentre retrospective cohort study of COVID-19 patients with AIS who presented to 30 stroke centres in the USA and Canada between 14 March and 30 August 2020. The primary endpoint was poor functional outcome, defined as a modified Rankin Scale (mRS) of 5 or 6 at discharge. Secondary endpoints include favourable outcome (mRS ≤2) and mortality at discharge, ordinal mRS (shift analysis), symptomatic intracranial haemorrhage (sICH) and occurrence of in-hospital complications. Results A total of 216 COVID-19 patients with AIS were included. 68.1% (147/216) were older than 60 years, while 31.9% (69/216) were younger. Median [IQR] National Institutes of Health Stroke Scale (NIHSS) at presentation was 12.5 (15.8), and 44.2% (87/197) presented with large vessel occlusion (LVO). Approximately 51.3% (98/191) of the patients had poor outcomes with an observed mortality rate of 39.1% (81/207). Age >60 years (aOR: 5.11, 95% CI 2.08 to 12.56, p<0.001), diabetes mellitus (aOR: 2.66, 95% CI 1.16 to 6.09, p=0.021), higher NIHSS at admission (aOR: 1.08, 95% CI 1.02 to 1.14, p=0.006), LVO (aOR: 2.45, 95% CI 1.04 to 5.78, p=0.042), and higher NLR level (aOR: 1.06, 95% CI 1.01 to 1.11, p=0.028) were significantly associated with poor functional outcome. Conclusion There is relationship between COVID-19-associated AIS and severe disability or death. We identified several factors which predict worse outcomes, and these outcomes were more frequent compared to global averages. We found that elevated neutrophil-to-lymphocyte ratio, rather than D-Dimer, predicted both morbidity and mortality.

Stroke–Heart Syndrome: Recent Advances and Challenges

Abstract: Abstract After ischemic stroke, there is a significant burden of cardiovascular complications, both in the acute and chronic phase. Severe adverse cardiac events occur in 10% to 20% of patients within the first few days after stroke and comprise a continuum of cardiac changes ranging from acute myocardial injury and coronary syndromes to heart failure or arrhythmia. Recently, the term stroke–heart syndrome was introduced to provide an integrated conceptual framework that summarizes neurocardiogenic mechanisms that lead to these cardiac events after stroke. New findings from experimental and clinical studies have further refined our understanding of the clinical manifestations, pathophysiology, and potential long‐term consequences of the stroke–heart syndrome. Local cerebral and systemic mediators, which mainly involve autonomic dysfunction and increased inflammation, may lead to altered cardiomyocyte metabolism, dysregulation of (tissue‐resident) leukocyte populations, and (micro‐) vascular changes. However, at the individual patient level, it remains challenging to differentiate between comorbid cardiovascular conditions and stroke‐induced heart injury. Therefore, further research activities led by joint teams of basic and clinical researchers with backgrounds in both cardiology and neurology are needed to identify the most relevant therapeutic targets that can be tested in clinical trials.

Advances in Recurrent Stroke Prevention: Focus on Antithrombotic Therapies.

Abstract: The past decade has seen significant advances in stroke prevention. These advances include new antithrombotic agents, new options for dyslipidemia treatment, and novel techniques for surgical stroke prevention. In addition, there is greater recognition of the benefits of multifaceted interventions, including the role of physical activity and dietary modification. Despite these advances, the aging of the population and the high prevalence of key vascular risk factors pose challenges to reducing the burden of stroke. Using a cause-based framework, current approaches to prevention of cardioembolic, cryptogenic, atherosclerotic, and small vessel disease stroke are outlined in this paper. Special emphasis is given to recent trials of antithrombotic agents, including studies that have tested combination treatments and responses according to genetic factors.

Microembolism and Other Links Between Migraine and Stroke

Abstract: Migraine and stroke are highly prevalent diseases with a high effect on quality of life, with multiple epidemiologic, pathophysiologic, clinical, and prognostic areas of overlap. Migraine is a risk factor for stroke. This risk is explained by common risk factors, migraine-specific mechanisms, and non–migraine-specific mechanisms that have a relevant role in patients with migraine with aura (e.g., atrial fibrillation and paradoxical embolism through a patent foramen ovale). Another important link between migraine aura and ischemic stroke is cardiac embolism. Cardioembolism is the most frequent cause of ischemic stroke, and increasing evidence suggests that microembolism, predominantly but not exclusively originating in the heart, is a contributing mechanism to the development of migraine aura. In this review, we discuss epidemiologic aspects of the association between migraine and ischemic stroke, the clinical presentation of ischemic strokes in patients with migraine, and the differentiation between migrainous and nonmigrainous infarctions. After that, we review migraine-specific and non–migraine-specific stroke mechanisms. We then review updated preclinical and clinical data on microembolism as a cause of migraine aura. In the last section, we summarize knowledge gaps and important areas to explore in future research. The review includes a clinical vignette with a discussion of the most relevant topics addressed.

Case-Fatality and Functional Outcome after Subarachnoid Hemorrhage (SAH) in INternational STRoke oUtComes sTudy (INSTRUCT).

Abstract: Background There are few large population-based studies of outcomes after subarachnoid hemorrhage (SAH) than other stroke types. Methods We pooled data from 13 population-based stroke incidence studies (10 studies from the INternational STRroke oUtComes sTudy (INSTRUCT) and 3 new studies; N=657). Primary outcomes were case-fatality and functional outcome (modified Rankin scale score 3-5 [poor] vs. 0-2 [good]). Harmonized patient-level factors included age, sex, health behaviours (e.g. current smoking at baseline), comorbidities (e.g.history of hypertension), baseline stroke severity (e.g. NIHSS >7) and year of stroke. We estimated predictors of case-fatality and functional outcome using Poisson regression and generalized estimating equations using log-binomial models respectively at multiple timepoints. Results Case-fatality rate was 33% at 1 month, 43% at 1 year, and 47% at 5 years. Poor functional outcome was present in 27% of survivors at 1 month and 15% at 1 year. In multivariable analysis, predictors of death at 1-month were age (per decade increase MRR 1.14 [1.07-1.22]) and SAH severity (MRR 1.87 [1.50-2.33]); at 1 year were age (MRR 1.53 [1.34-1.56]), current smoking (MRR 1.82 [1.20-2.72]) and SAH severity (MRR 3.00 [2.06-4.33]) and; at 5 years were age (MRR 1.63 [1.45-1.84]), current smoking (MRR 2.29 [1.54-3.46]) and severity of SAH (MRR 2.10 [1.44-3.05]). Predictors of poor functional outcome at 1 month were age (per decade increase RR 1.32 [1.11-1.56]) and SAH severity (RR 1.85 [1.06-3.23]), and SAH severity (RR 7.09 [3.17-15.85]) at 1 year. Conclusion Although age is a non-modifiable risk factor for poor outcomes after SAH, however, severity of SAH and smoking are potential targets to improve the outcomes.

Atrial cardiopathy and cognitive impairment

Abstract: Cognitive impairment involves complex interactions between multiple pathways and mechanisms, one of which being cardiac disorders. Atrial cardiopathy (AC) is a structural and functional disorder of the left atrium that may be a substrate for other cardiac disorders such as atrial fibrillation (AF) and heart failure (HF). The association between AF and HF and cognitive decline is clear; however, the relationship between AC and cognition requires further investigation. Studies have shown that several markers of AC, such as increased brain natriuretic peptide and left atrial enlargement, are associated with an increased risk for cognitive impairment. The pathophysiology of cognitive decline in patients with AC is not yet well understood. Advancing our understanding of the relationship between AC and cognition may point to important treatable targets and inform future therapeutic advancements. This review presents our current understanding of the diagnosis of AC, as well as clinical characteristics and potential pathways involved in the association between AC and cognitive impairment.

Chronic Oral Anticoagulation Therapy and Prognosis of Patients Admitted to Hospital for COVID-19: Insights from the HOPE COVID-19 Registry

Abstract: Background Most evidence regarding anticoagulation and COVID-19 refers to the hospitalization setting, but the role of oral anticoagulation (OAC) before hospital admission has not been well explored. We compared clinical outcomes and short-term prognosis between patients with and without prior OAC therapy who were hospitalized for COVID-19. Methods Analysis of the whole cohort of the HOPE COVID-19 Registry which included patients discharged (deceased or alive) after hospital admission for COVID-19 in 9 countries. All-cause mortality was the primary endpoint. Study outcomes were compared after adjusting variables using propensity score matching (PSM) analyses. Results 7698 patients were suitable for the present analysis (675 (8.8%) on OAC at admission: 427 (5.6%) on VKAs and 248 (3.2%) on DOACs). After PSM, 1276 patients were analyzed (638 with OAC; 638 without OAC), without significant differences regarding the risk of thromboembolic events (OR 1.11, 95% CI 0.59–2.08). The risk of clinically relevant bleeding (OR 3.04, 95% CI 1.92–4.83), as well as the risk of mortality (HR 1.22, 95% CI 1.01–1.47; log-rank p value = 0.041), was significantly increased in previous OAC users. Amongst patients on prior OAC only, there were no differences in the risk of clinically relevant bleeding, thromboembolic events, or mortality when comparing previous VKA or DOAC users, after PSM. Conclusion Hospitalized COVID-19 patients on prior OAC therapy had a higher risk of mortality and worse clinical outcomes compared to patients without prior OAC therapy, even after adjusting for comorbidities using a PSM. There were no differences in clinical outcomes in patients previously taking VKAs or DOACs. This trial is registered with NCT04334291/EUPAS34399.

Prolonged Cardiac Monitoring for Atrial Fibrillation Detection After Stroke

Abstract: Prolonged cardiac monitoring (PCM) is one of the pillars of the diagnostic workup for patients with ischemic stroke. However, the ideal duration of PCM for atrial fibrillation (AF) detection after stroke has not been established. Recommendations on PCM duration after stroke vary across clinical guidelines from nonspecific long-term monitoring1 to 2 weeks.2 Furthermore, AF screening strategies after stroke vary widely in clinical practice,3,4 possibly due to differences in access to monitoring technologies,4 vague recommendations from clinical guidelines,1 and lack of data supporting a specific duration of monitoring.

Atrial fibrillation detection after stroke: Is all created equal?

Abstract: 1Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 2Heart & Brain Laboratory, Western University, London, ON, Canada 3Departments of Epidemiology and Biostatistics and Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada 4Robarts Research Institute, Western University, London, ON, Canada 5Lawson Health Research Institute, London, ON, Canada 6Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada 7Division of Neurology, University of British Columbia, Vancouver, BC, Canada 8Vancouver Stroke Program, Vancouver General Hospital, Vancouver, BC, Canada

Deep learning prediction of stroke thrombus red blood cell content from multiparametric MRI.

Abstract: BACKGROUND AND PURPOSE Thrombus red blood cell (RBC) content has been shown to be a significant factor influencing the efficacy of acute ischemic stroke treatment. In this study, our objective was to evaluate the ability of convolutional neural networks (CNNs) to predict ischemic stroke thrombus RBC content using multiparametric MR images. MATERIALS AND METHODS Retrieved stroke thrombi were scanned ex vivo using a three-dimensional multi-echo gradient echo sequence and histologically analyzed. 188 thrombus R2*, quantitative susceptibility mapping and late-echo GRE magnitude image slices were used to train and test a 3-layer CNN through cross-validation. Data augmentation techniques involving input equalization and random image transformation were employed to improve network performance. The network was assessed for its ability to quantitatively predict RBC content and to classify thrombi into RBC-rich and RBC-poor groups. RESULTS The CNN predicted thrombus RBC content with an accuracy of 62% (95% CI 48-76%) when trained on the original dataset and improved to 72% (95% CI 60-84%) on the augmented dataset. The network classified thrombi as RBC-rich or poor with an accuracy of 71% (95% CI 58-84%) and an area under the curve of 0.72 (95% CI 0.57-0.87) when trained on the original dataset and improved to 80% (95% CI 69-91%) and 0.84 (95% CI 0.73-0.95), respectively, on the augmented dataset. CONCLUSIONS The CNN was able to accurately predict thrombus RBC content using multiparametric MR images, and could provide a means to guide treatment strategy in acute ischemic stroke.

Oral anticoagulant decreases stroke recurrence in patients with atrial fibrillation detected after stroke

Abstract: Background Atrial fibrillation detected after stroke (AFDAS) has a lower risk of ischemic stroke recurrence than known atrial fibrillation (KAF). While the benefit of oral anticoagulants (OAC) for preventing ischemic stroke recurrence in KAF is well established, their role in patients with AFDAS is more controversial. This study aimed to evaluate the association between OAC use and the risk of recurrent ischemic stroke in patients with AFDAS in a real-world setting. Methods This nationwide retrospective cohort study was conducted using the Taiwan National Health Insurance Research Database. Patients hospitalized with a first-ever ischemic stroke and AFDAS confirmed within 30 days after hospitalization were assigned to OAC and non-OAC cohorts. Inverse probability of treatment weighting was applied to balance the baseline characteristics of the cohorts. The primary outcome was ischemic stroke recurrence. Secondary outcomes were intracranial hemorrhage (ICH), death, and the composite outcome of “ischemic stroke recurrence, ICH, or death.” Multivariate Cox proportional hazard models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). Results A total of 4,508 hospitalized patients with stroke and AFDAS were identified. Based on OAC use, 2,856 and 1,652 patients were assigned to the OAC and non-OAC groups, respectively. During the follow-up period (median duration, 2.76 years), the OAC cohort exhibited a lower risk of ischemic stroke recurrence (aHR, 0.84; 95% CI, 0.70–0.99), death (aHR, 0.65; 95% CI, 0.58–0.73), and composite outcome (aHR, 0.70; 95% CI, 0.63–0.78) than did the non-OAC cohort. The risk of ICH (aHR, 0.96; 95% CI, 0.62–1.50) was not significantly different between the two cohorts. Conclusion OAC use in patients with AFDAS was associated with reduced risk of ischemic stroke recurrence, without an increased risk of ICH. This supports current guidelines recommending OACs for secondary stroke prevention in patients with AF, regardless of the time of diagnosis.

Abstract 12706: Stroke Risk and Oral Anticoagulation Use With Extended Cardiac Monitoring for Atrial Fibrillation versus Usual Care: A Systematic Review With Meta-Analysis

Abstract: INTRODUCTION: Prolonged cardiac monitoring is frequently used to detect atrial fibrillation (AF) in high-risk populations, with the goal of preventing thromboembolic events. We sought to determine the impact of prolonged cardiac monitoring on the incidence of stroke and systemic embolism (SSE) or transient ischemic attack (TIA) METHODS: We performed a systematic review and meta-analysis of randomized trials evaluating prolonged monitoring versus usual care (PROSPERO #CRD42021277611). Studies were identified through CENTRAL, MEDLINE, and Embase. We included studies with ≥100 participants and ≥30 days follow-up. The primary outcome was a composite of SSE/TIA, as reported in the original trials. Secondary outcomes included AF detection, oral anticoagulation (OAC) initiation, and major bleeding. Sensitivity analysis examining the impact of monitoring device, and indication for monitoring were performed. Meta-analyses were performed with R using a random-effects model. RESULTS: From 1411 records, we included 9 RCTs (n = 10,205). Mean age was 70 years, 40% were female, and mean CHADS2 score was 4.0. Studies used implantable cardiac monitors (n = 4), external cardiac monitors (n = 3), or handheld ECG devices (n = 2). Study populations included post-stroke (n = 5), high risk for AF or stroke (n = 2), and post-cardiac surgery (n = 1). Mean follow-up was 16 months (range 3-65). Extended monitoring did not significantly reduce the primary outcome (Figure, random effects risk ratio [RR] 0.87, 95% confidence interval [CI] 0.72-1.06, I2 = 0%) or its individual components. Extended monitoring increased AF detection (RR 4.56, 95% CI 3.01-6.92, I2 = 65%) and OAC usage (RR 2.25, 95% CI 2.01-2.53, I2 = 0%), but did not impact major bleeding (RR 1.23, 95% CI 0.84-1.82, I2 = 0%). CONCLUSION: Prolonged monitoring was associated with increased AF detection and OAC use, without significantly reducing the occurrence of thromboembolic events.

Abstract WMP97: Differences Between Atrial Fibrillation Detected Before And After Stroke And Tia: A Systematic Review & Meta-analysis

Abstract: Background and Purpose: Recent evidence suggests that patients with atrial fibrillation (AF) detected after stroke ( AFDAS ) may have a lower prevalence of cardiovascular comorbidities and lower risk of stroke recurrence than AF known before stroke ( KAF ). We performed a systematic search and meta-analysis to compare the characteristics of AFDAS and KAF. Methods: We searched PubMed, Scopus, and EMBASE for articles reporting differences between AFDAS and KAF until 30-June-2021. We performed random- or fixed-effects meta-analyses to evaluate differences between AFDAS and KAF in demographic factors, vascular risk factors, prevalent vascular comorbidities, structural heart disease, stroke severity, insular cortex involvement, stroke recurrence, and death. Results: We included 21 studies comprising 22,566 patients with ischemic stroke or transient ischemic attack. Patients with AFDAS had a lower CHA2DS2-VASc score (standardized mean difference [ SMD ] -0.47, 95% confidence interval [ 95% CI ] -0.60, -0.34), and lower prevalence of vascular comorbidities including coronary artery disease (odds ratio [ OR ] 0.50, 95%CI 0.42, 0.61), congestive heart failure (OR 0.37, 95% CI 0.31, 0.44), peripheral artery disease (OR 0.44, 95%CI 0.29, 0.68), and previous stroke (RD 0.38, 95% CI 0.25, 0.58). Patients with AFDAS had a higher left ventricular ejection fraction (SMD 0.25, 95% CI 0.20, 0.30) and smaller mean atrial diameter (SMD -0.65, 95% CI -0.99, -0.31) than those with KAF. There were no differences in age, sex, stroke severity, or death rates between AFDAS and KAF. There were not enough studies to report differences in insular cortex involvement between AF types. Conclusions: We found significant differences in the prevalence of vascular comorbidities, structural heart disease, and stroke recurrence rates between AFDAS and KAF, suggesting that they constitute different clinical entities within the AF spectrum.

The Challenging Puzzle of Migraine, Age, and Vascular Health

Abstract: The association between migraine and cardiovascular risk is well established but poorly understood.1 Migraine, especially with aura, is an independent risk factor for stroke and ischemic heart disease.1 Although potential migraine-related mechanisms may explain a specific brain vulnerability to ischemia, the pathophysiologic pathways leading to an increased risk of cardiovascular events remain unclear. Because of differences in methodology and definitions, current evidence is conflicting and does not provide unbiased support to the role of traditional risk factors as drivers of cardiovascular risk in patients with migraine.2

CONTEMPORARY Stroke– Heart Syndrome: Recent Advances and Challenges

Abstract: : After ischemic stroke, there is a significant burden of cardiovascular complications, both in the acute and chronic phase. Severe adverse cardiac events occur in 10% to 20% of patients within the first few days after stroke and comprise a continuum of cardiac changes ranging from acute myocardial injury and coronary syndromes to heart failure or arrhythmia. Recently, the term stroke– heart syndrome was introduced to provide an integrated conceptual framework that summarizes neurocardiogenic mechanisms that lead to these cardiac events after stroke. New findings from experimental and clinical studies have further refined our understanding of the clinical manifestations, pathophysiology, and potential long- term consequences of the stroke– heart syndrome. Local cerebral and systemic mediators, which mainly involve autonomic dysfunction and increased inflammation, may lead to altered cardiomyocyte metabolism, dysregulation of (tissue- resident) leukocyte populations, and (micro- ) vascular changes. However, at the individual patient level, it remains challenging to differentiate between comorbid cardiovascular conditions and stroke- induced heart injury. Therefore, further research activities led by joint teams of basic and clinical researchers with backgrounds in both cardiology and neurology are needed to identify the most relevant therapeutic targets that can be tested in clinical trials.

Ex Vivo Thrombus Magnetic Resonance Imaging Features and Patient Clinical Data Enable Prediction of Acute Ischemic Stroke Cause

Abstract: HomeStroke: Vascular and Interventional NeurologyVol. 2, No. 2Ex Vivo Thrombus Magnetic Resonance Imaging Features and Patient Clinical Data Enable Prediction of Acute Ischemic Stroke Cause Open AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citations ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toSupplementary MaterialsOpen AccessResearch ArticlePDF/EPUBEx Vivo Thrombus Magnetic Resonance Imaging Features and Patient Clinical Data Enable Prediction of Acute Ischemic Stroke Cause Spencer D. Christiansen, PhD, Junmin Liu, PhD, Maria Bres Bullrich, MD, Manas Sharma, MD, Sachin K. Pandey, MD, Melfort Boulton, MDPhD, Sebastian Fridman, MDMPH, Luciano A. Sposato, MDMBA and Maria Drangova, PhD Spencer D. ChristiansenSpencer D. Christiansen https://orcid.org/0000-0002-9497-6789 , Imaging Research Laboratories, , Robarts Research Institute, , London, , Ontario, , Canada, , Department of Medical Biophysics, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Junmin LiuJunmin Liu , Imaging Research Laboratories, , Robarts Research Institute, , London, , Ontario, , Canada, Search for more papers by this author , Maria Bres BullrichMaria Bres Bullrich , Department of Clinical Neurological Sciences, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Manas SharmaManas Sharma , Department of Medical Imaging, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Sachin K. PandeySachin K. Pandey , Department of Medical Imaging, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Melfort BoultonMelfort Boulton , Department of Clinical Neurological Sciences, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Sebastian FridmanSebastian Fridman , Department of Clinical Neurological Sciences, , Western University, , London, , Ontario, , Canada, Search for more papers by this author , Luciano A. SposatoLuciano A. Sposato , Department of Clinical Neurological Sciences, , Western University, , London, , Ontario, , Canada, Search for more papers by this author and Maria DrangovaMaria Drangova *Correspondence to: Maria Drangova, PhD, Imaging Research Laboratories, Robarts Research Institute, Western University, 1151 Richmond Street, London, Ontario N6A 2B7, Canada.E‐mail: E-mail Address: [email protected] , Imaging Research Laboratories, , Robarts Research Institute, , London, , Ontario, , Canada, , Department of Medical Biophysics, , Western University, , London, , Ontario, , Canada, Search for more papers by this author Originally published31 Jan 2022https://doi.org/10.1161/SVIN.121.000157Stroke: Vascular and Interventional Neurology. 2022;2:e000157Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: March 8, 2022: Previous Version of Record February 1, 2022: Ahead of Print AbstractDownload figureThe cause of ischemic stroke often remains elusive even after full stroke workup is completed. Cardioembolic mechanisms in particular are frequently presumed but challenging to definitively diagnose. Quantitative thrombus texture analysis is emerging as a powerful tool for stroke characterization, having shown the ability to predict response to stroke treatment,1 but its ability to predict stroke cause and complement machine learning models built from standard clinical features has not been studied.2, 3 The purpose of this study is to evaluate the ability of radiomics features extracted from quantitative magnetic resonance images of retrieved ischemic stroke thrombi (R2*(=1/T2*), quantitative susceptibility mapping, and fat fraction) to improve the accuracy of machine learning models built from clinical data for the prediction of cardioembolic stroke.MethodsInstitutional research ethics board approval was obtained for this study; data are available from the corresponding author on reasonable request.Patients with acute ischemic stroke with cardioembolic or large artery atherosclerosis causes determined using TOAST criteria and thrombi retrieved through endovascular therapy were consecutively enrolled into training (February 2016–November 2017; N=49) and validation (November 2019–March 2020; N=11) cohorts. Summary clinical details of each cohort are included in Supplemental Table SI (available from: ir.lib.uwo.ca/vascularpub/59). Patients or their substitute decision‐maker gave informed consent after the procedure was completed and ≥1 thrombi were retrieved. A dual‐echo‐train gradient echo sequence4 was acquired on the thrombi ex vivo with 0.94×0.94×1.0 mm3 resolution and a scan time of 5 minutes 33 seconds on a 3T clinical scanner. A balanced steady‐state gradient echo sequence (FIESTA‐C) with identical resolution was also acquired and used for thrombus segmentation (scan time: 2 minutes 47 seconds). R2*, quantitative susceptibility mapping, and fat fraction maps were generated from the multiecho gradient echo data using previously described methods.4 Random forest classifier models were built to differentiate between cardioembolic and large artery atherosclerosis stroke on a per‐thrombus basis using patient clinical data features available from the basic stroke workup and quantitative ex vivo thrombus magnetic resonance texture features extracted from R2*, quantitative susceptibility mapping, and fat fraction maps. Models were built in MATLAB (The MathWorks, Inc) using code modified from Vallières et al.5 Tested texture features are listed in Supplemental Table SII (available from: ir.lib.uwo.ca/vascularpub/59). Feature selection was performed on the training cohort using multivariate logistic regression for texture and univariate statistics for clinical features, respectively. Models were evaluated on the entire validation cohort, and area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy for all thrombi within the cohort (n=18) were determined.ResultsWithin the training cohort, age, smoking, left atrial enlargement, left ventricular ejection fraction, cardiac wall motion abnormalities, and triglyceride levels differed between patients with cardioembolic and large artery atherosclerosis (all P<0.05; Supplemental Table SIII, ir.lib.uwo.ca/vascularpub/59) and were thus included in the clinical model. This model predicted cardioembolic mechanism in the validation cohort with an AUC of 0.80 (95% CI, 0.45–1), sensitivity of 80% (95% CI, 52%–100%), specificity of 100%, and accuracy of 91% (95% CI, 78%–100%). Selected thrombus imaging texture features were quantitative susceptibility mapping global skewness, gray‐level run‐length matrix run‐length variance, gray‐level size zone matrix gray‐level nonuniformity, and fat fraction global variance, and their inclusion increased model AUC, sensitivity, and accuracy to 0.89 (95% CI, 0.68–1), 88% (95% CI, 65%–100%), and 94% (95% CI, 84%–100%), respectively, while maintaining 100% specificity. A separate model built using only thrombus texture features produced an AUC, sensitivity, specificity, and accuracy of 0.63 (95% CI, 0.35–0.9), 75 (95% CI, 45%–100%), 60 (95% CI, 30%–90%), and 67% (95% CI, 45%–89%), respectively. A diagram displaying the texture feature extraction process for 2 representative thrombi and demonstrating the ability for imaging information to improve clinical model predictions is shown in Figure 1.Download figureDownload PowerPointFigure 1. Overview of model processing steps for representative cardioembolic (A) and large artery atherosclerosis (B) thrombi. Segmentations derived from FIESTA‐C images are applied to naturally coregistered quantitative susceptibility mapping (QSM) and fat fraction (FF) maps, which are binned before extraction of the histograms and matrices used to derive the 4 predictive texture features for model input. In (A), a patient with known atrial fibrillation did not undergo echocardiography and was missing multiple variables included in the clinical model, uncommon in this cohort but typical of clinical data in general, which resulted in a weak cardioembolic cause prediction that was greatly improved through the addition of thrombus texture information. In (B), all patient clinical features were available and the addition of thrombus texture information improved the model's confidence in the identification of a noncardioembolic cause. GLN indicates gray‐level nonuniformity; GLRLM, gray‐level run‐length matrix; GLSZM, gray‐level size zone matrix; LA, left atrial; LV, left ventricular; RLV, run‐length variance; and WM, wall motion.DiscussionThis study suggests that thrombus magnetic resonance imaging can improve the accuracy of clinical data for the prediction of cardioembolic stroke mechanisms. The combination of thrombus imaging texture and baseline clinical data features discriminated between stroke sources with exceptional accuracy.Recently, a large study by Kamel et al2 developed a machine learning model for stroke cause prediction using only clinical variables. Similar to our study, the model identified age, left atrial enlargement, ejection fraction, and smoking history as important predictors of cardioembolic cause, and yielded an AUC of 0.85 akin to our clinical‐only model. Here, the addition of imaging features improved the performance of the clinical model, suggesting that thrombus imaging information can complement clinical data for this task. This study is limited by its small sample size and ex vivo design; only patients who underwent successful endovascular therapy could be included and alterations to thrombi during thrombolysis, retrieval, or storage could have affected the imaging values. The study is also limited by the lack of a histological validation of imaging features and the inclusion of multiple cardioembolic stroke subtypes in the cardioembolic group. The generalizability of the model to thrombi imaged in vivo remains to be evaluated. Nonetheless, this proof‐of‐concept study suggests that thrombus magnetic resonance texture features can improve the accuracy of clinical features alone for predicting cardioembolic cause among patients with acute ischemic stroke.FundingThis work was supported by Canadian Institutes of Health Research grant number CIHR #PJT 153411.DisclosuresS.D.C., J.L., M.B.B., M.S., S.K.P., M.B., S.F., and M.D. report no disclosures relevant to the article. L.A.S. reports speaker honoraria from Boehringer Ingelheim, Pfizer, Gore, and Bayer, and research/quality improvement grants from Boehringer Ingelheim and Bayer. L.A.S. is a member of the editorial board of Neurology, Stroke, and World Stroke Academy.AcknowledgmentsThe authors wish to thank Justina Diaz Legaspe for recruiting and consenting participants; Ada Manini, Leanne Sperlich, Jon Collier, Sarah Sitts, Barb Lehrbass, Andrew Kernohan, Elizabeth Cesarin, and Jill Uitvlugt for storage of thrombi and facilitating sample transfer; all interventional neuroradiologists at London Health Sciences Centre for thrombus retrieval; and Trevor Wade for implementation of the pulse sequence.Footnotes*Correspondence to: Maria Drangova, PhD, Imaging Research Laboratories, Robarts Research Institute, Western University, 1151 Richmond Street, London, Ontario N6A 2B7, Canada.E‐mail: [email protected]ca[Correction added on 13th May 2022, after online publication: The copyright line is changed].References1 Hofmeister J, Bernava G, Rosi A, Vargas MI, Carrera E, Montet X, Burgermeister S, Poletti PA, Platon A, Lovblad KO. Clot‐based radiomics predict a mechanical thrombectomy strategy for successful recanalization in acute ischemic stroke. Stroke. 2020; 51:2488–2494.LinkGoogle Scholar2 Kamel H, Navi BB, Parikh NS, Merkler AE, Okin PM, Devereux RB, Weinsaft JW, Kim J, Cheung JW, Kim LK. Machine learning prediction of stroke mechanism in embolic strokes of undetermined source. Stroke. 2020; 51:e203–e210.LinkGoogle Scholar3 Guan W, Ko D, Khurshid S, Trisini Lipsanopoulos AT, Ashburner JM, Harrington LX, Rost NS, Atlas SJ, Singer DE, McManus DD. Automated electronic phenotyping of cardioembolic stroke. Stroke. 2021; 52:181–189.LinkGoogle Scholar4 Liu J, Christiansen SD, Drangova M. Single multi‐echo GRE acquisition with short and long echo spacing for simultaneous quantitative mapping of fat fraction, B0 inhomogeneity, and susceptibility. NeuroImage. 2018; 172:703–717.Google Scholar5 Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG‐PET and MRI texture features for the prediction of lung metastases in soft‐tissue sarcomas of the extremities. Phys Med Biol. 2015; 60:5471–5496.Google Scholar Previous Back to top Next FiguresReferencesRelatedDetails March 2022Vol 2, Issue 2Article InformationMetrics © 2022 The Authors. Published on behalf of the American Heart Association, Inc., and the Society of Vascular and Interventional Neurology by Wiley Periodicals LLC.This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.https://doi.org/10.1161/SVIN.121.000157 Manuscript receivedAugust 11, 2021Manuscript acceptedDecember 29, 2021Originally publishedJanuary 31, 2022 Keywordsmagnetic resonance imagingstrokethrombosisischemic strokemachine learningthromboembolismischemiahumansPDF download SubjectsEtiologyIschemic StrokeMachine Learning and Artificial IntelligenceMagnetic Resonance Imaging (MRI)

Epilepsy Surgery in Adult Stroke Survivors with New-Onset Drug-Resistant Epilepsy.

Abstract: BACKGROUND Despite its effectiveness, surgery for drug-resistant epilepsy is underutilized. However, whether epilepsy surgery is also underutilized among patients with stroke-related drug-resistant epilepsy is unclear. Therefore, our objectives were to estimate the rates of epilepsy surgery assessment and receipt among patients with stroke-related drug-resistant epilepsy and to identify factors associated with these outcomes. METHODS We used linked health administrative databases to conduct a population-based retrospective cohort study of adult Ontario, Canada residents discharged from an Ontario acute care institution following the treatment of a stroke between January 1, 1997, and December 31, 2020, without prior evidence of seizures. We excluded patients who did not subsequently develop drug-resistant epilepsy and those with other epilepsy risk factors. We estimated the rates of epilepsy surgery assessment and receipt by March 31, 2021. We planned to use Fine-Gray subdistribution hazard models to identify covariates independently associated with our outcomes, controlling for the competing risk of death. RESULTS We identified 265,081 patients who survived until discharge following inpatient stroke treatment, 1,902 (0.7%) of whom subsequently developed drug-resistant epilepsy (805 women; mean age: 67.0 ± 13.1 years). Fewer than six (≤0.3%) of these patients were assessed for or received epilepsy surgery before the end of follow-up (≤55.5 per 100,000 person-years). Given that few outcomes were identified, we could not proceed with the multivariable analyses. CONCLUSIONS Patients with stroke-related drug-resistant epilepsy are infrequently considered for epilepsy surgery that could reduce morbidity and mortality.