American Heart Association

diagnosis and imaging

Left Atrial Appendage Thrombus in Patients with Ischemic Stroke as Marker of Atrial Fibrillation?

Wern Yew Ding, MBChB

Senadeera SC, Palmer DG, Keenan R, Beharry J, Yuh Lim J, Hurrell MA, Mouthaan P, Fink JN, Wilson D, Lim A, Wu TY. Left Atrial Appendage Thrombus Detected During Hyperacute Stroke Imaging Is Associated With Atrial Fibrillation. Stroke. 2020;51:3760–3764.

Atrial fibrillation (AF) is an established risk factor for thromboembolic events, including ischemic stroke. Therefore, identification of patients with this arrhythmia is important to facilitate the implementation of stroke prevention therapy using oral anticoagulation. Nonetheless, as a significant proportion of patients with AF remain asymptomatic, it remains largely under-diagnosed in the general population. Given that the source of emboli in the majority of AF-related strokes originates from the left atrial appendage (LAA), inclusion of this structure in imaging protocols may have a role in aiding the diagnosis of AF.

In a recent retrospective study of consecutive patients with ischemic stroke or transient ischemic attack, Senadeera and colleagues investigated the prevalence of computed tomography angiography (CTA)-detected LAA thrombus during hyperacute stroke imaging and evaluated the association between LAA thrombus and AF. The imaging protocol consisted of non-contrast CT, followed by CT perfusion and CTA from aortic arch to vertex. Two experienced physicians and pre-defined measures were used to assess for LAA thrombus on these scans.

External Validation of the Edinburgh Criteria for Cerebral Amyloid Angiopathy

Walter Valesky, MD

van Etten ES, Kaushik K, van Zwet EW, Voigt S, van Walderveen MAA, van Buchem MA, Terwindt GM, Wermer MJH. Sensitivity of the Edinburgh Criteria for Lobar Intracerebral Hemorrhage in Hereditary Cerebral Amyloid Angiopathy. Stroke. 2020;51:3608–3612.

Limitations in our knowledge of cerebral amyloid angiopathy (CAA) persist due to relatively small study sample sizes and a requirement for pathological specimens to confirm a diagnosis. The Edinburgh criteria is the most recent decision instrument developed to assist in the pre-mortem diagnosis of this disorder. In their logistic regression model, Rodrigues et al. utilized genetic factors (APOE ε4 genotype) and computed tomography (CT) findings (finger-like projections and subarachnoid hemorrhage) to attain a high degree of sensitivity and specificity.1 However, their cohort relied on autopsy specimens for confirmation of moderate-to-severe CAA. 

To this end, van Etten et al. recruited patients with Dutch-type CAA (D-CAA) for analysis. D-CAA, a hereditary variant of CAA, causes similar radiographic features as CAA with an accelerated clinical course, and most importantly, does not require a tissue-based confirmation. Using patients with D-CAA, the investigators evaluated the aforementioned CT variables in this validation study. 

Collaterals Impact on Ischemic Area: Does the Size Matter?

Elena Zapata-Arriaza, MD

Al-Dasuqi K, Payabvash S, Torres-Flores GA, Strander SM, Nguyen CK, Peshwe KU, Kodali S, Silverman A, Malhotra A, Johnson MH, et al. Effects of Collateral Status on Infarct Distribution Following Endovascular Therapy in Large Vessel Occlusion Stroke. Stroke. 2020;51:e193–e202.

Collateral status has been related to impact on infarct size after ischemic stroke (IS) recanalization. However, the smaller final infarct size is not always related to a good clinical situation, which seems to be related to the eloquence of the affected area, rather than the volume of the ischemic area itself. The present scientific work aims to evaluate the relation between collateral status and reperfusion degree on final infarct distribution and clinical outcome after IS due to large vessel occlusion (LVO).

Al-Dasuqi and colleagues performed a single center retrospective analysis of all patients with LVO who were treated with endovascular treatment between 2013-2019. The authors collected clinical, demographic and radiological data. They applied a multivariate voxel-wise general linear model to correlate the distribution of final infarction with collateral status and degree of reperfusion. Early favorable outcome was defined as a discharge modified Rankin Scale score ≤2.

ISC 2021: Novel Imaging Techniques in ICAD — Beyond the Stenosis

Song J. Kim, MD

International Stroke Conference 2021
March 17–19, 2021
Session: Advanced Imaging in Intracranial Atherosclerotic Disease: Misnomer or Game-Changer? (24, OnDemand)

A common stroke mechanism accounting for 20-30% of the ischemic strokes worldwide, intracranial atherosclerotic disease (ICAD) is a diagnosis that primarily relies on visualization of luminal narrowing on CTA/MRA. This session expanded upon cutting-edge advances in imaging of ICAD, specifically in revealing plaque morphology, collateral status, and cerebrovascular reserve distal to the stenosis of the culprit lesion.

Before the panel delved into discussion of the advanced imaging, Dr. Achala Vagal provided a comprehensive overview highlighting the limitations of the current conventional lumen-based imaging: failure to detect on-stenosing plaque, compensatory remodeling, and the status of distal flow and collateralization.

Endovascular Treatment in Ischemic Stroke: The Controversy About the Relevance of the Image-Defined Infarct Core

Tolga D. Dittrich, MD

Goyal M, Ospel JM, Menon B, Almekhlafi M, Jayaraman M, Fiehler J, Psychogios M, Chapot R, van der Lugt A, Liu J, et al. Challenging the Ischemic Core Concept in Acute Ischemic Stroke Imaging. Stroke. 2020;51:3147–3155.

Endovascular treatment (EVT) is a fundamental component of acute therapy for ischemic stroke due to large vessel occlusion. Although the clinical decision for or against EVT is individual and multifactorial, it is mainly based on radiological parameters, especially the ischemic core’s visualization on neuroimaging. In their review, Goyal et al. address the practical difficulties in attributing image-defined core significance to EVT.

Clinically, the term “core” is commonly used as a synonym for infarcted brain tissue that can no longer be saved. However, the concept of a homogeneous infarction core is increasingly being challenged. The reality seems to be much more complicated, since the susceptibility of different cell and tissue types is variable, and, depending on the speed of reperfusion, there may be no, partial, or complete necrosis of the core area.

Author Interview: Dr. Rajat Dhar on “Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage”

Dr. Rajat Dhar, left, and Dr. Saurav Das
Dr. Rajat Dhar, left, and Dr. Saurav Das

A conversation with Dr. Rajat Dhar, MD, Associate Professor of Neurology and Neuro-critical care, Washington University School of Medicine, St. Louis, MO.

Interviewed by Dr. Saurav Das, MD, Fellow in Vascular Neurology, Washington University School of Medicine, St. Louis, MO.

They will be discussing the article “Automated Quantification of Reduced Sulcal Volume Identifies Early Brain Injury After Aneurysmal Subarachnoid Hemorrhage,” published in Stroke.

Dr. Das: Dr. Dhar, on behalf of the Blogging Stroke team, we welcome you to this author interview.  I read with great interest your paper pertaining to the automated estimation of selective sulcal volume (SSV) to quantify global cerebral edema (GCE) from early brain injury (EBI) in aneurysmal subarachnoid hemorrhage (aSAH). This is an important paper as our understanding of clinical outcomes following aSAH is shifting from vasospasm induced delayed cerebral ischemia (DCI) towards GCE from EBI. Also, we currently do not have the tools to measure GCE accurately.

This research uses a “deep learning-based approach” for the analysis of serial CT scans to measure SSV. Many of our readers may not be familiar with the use of artificial intelligence (AI) in image analysis. I will begin by requesting you to explain what deep learning is.

Dr. Dhar: Applications of artificial intelligence, specifically machine learning, to the realm of biomedical image analysis have been growing exponentially over the past few years. AI is well-suited to image analysis because, at its core, machine learning seeks to find patterns in data, and images are just patterns of intensity and location data. Machine learning algorithms can be trained to learn from labeled data. For example, to determine what regions of a scan represent blood vs. brain vs. CSF is called a segmentation task. We can use machine learning to perform a segmentation task on new imaging data. AI algorithms can perform image analysis in a fast and reproducible way, eliminating the need for time-intensive human input. They can measure volumes of similar brain structures over serial time points more objectively and accurately than one or more humans may be able to.

Personalizing ESUS: Using DNA Content of Thrombi to Identify Cardioembolic Stroke

Melanie R. F. Greenway, MD

Di Meglio L, Desilles J-P, Solonomenjanahary M, Labreuche J, Ollivier V, Dupont S, Deschildre C, Maacha MB, Consoli A, Lapergue B, et al. DNA Content in Ischemic Stroke Thrombi Can Help Identify Cardioembolic Strokes Among Strokes of Undetermined Cause. Stroke. 2020;51:2810–2816.

With cryptogenic stroke comprising 20-30% of all ischemic stroke, many researchers are investigating a variety of methods to de-mystify cryptogenic stroke. In this article, potential biomarkers of the clot retrieved from mechanical thrombectomy were compared to the known stroke cause to evaluate potential clot characteristics that may predict stroke cause.

Glycoprotein VI (GPVI), heme, and DNA content were used to evaluate platelet, red blood cell, and leukocyte content of a random sample of 250 thrombi from 1209 consecutive acute ischemic stroke patients who underwent mechanical thrombectomy. 

The thrombus specimens were grinded through a tissue lyser, and thrombus homogenates were recovered after centrifugation. RBC content was estimated using heme concentration. GPVI levels were used to estimate platelet concentration. DNA content was quantified as an estimation of leukocyte count.

Infarct Distribution Following Endovascular Therapy in Large Vessel Occlusion Stroke

Christopher Wilkins, MD

Al-Dasuqi K, Payabvash S, Torres-Flores GA, Strander SM, Nguyen CK, Peshwe KU, Kodali S, Silverman A, Malhotra A, Johnson MH, et al. Effects of Collateral Status on Infarct Distribution Following Endovascular Therapy in Large Vessel Occlusion Stroke. Stroke. 2020;51:e193–e202.

Endovascular therapy has become an invaluable tool in the treatment of acute ischemic stroke as it can provide significant improvement in the functional outcome of selected patients. Since its reception, studies have broadened the time window for endovascular therapy by using perfusion imaging during acute ischemic strokes to determine how much cerebral tissue is, or close to be, infarcted (i.e., the core) and comparing it to tissue which has reduced blood flow but is likely salvageable with reperfusion (i.e., the penumbra). The volume of the core, as well as ratio between core and penumbra, ultimately determines which patients are appropriate for endovascular therapy. Studies have shown that cerebral collateral circulation can be a major determinant of final infarct volume and can thus impact who would be deemed appropriate for thrombectomy. However, data on whether the status of collateral circulation impacts final clinical outcome in those undergoing thrombectomy remains discrepant.

In this retrospective study, Al-Dasuqi et al. investigated how collateral status impacts final infarct size, as well as functional outcomes, in those with successful and unsuccessful recanalization following endovascular therapy with either mechanical thrombectomy or intra-arterial thrombolytic drug delivery.  The authors selected patients who: had evidence of large vessel occlusion on CTA in the ICA or MCA at the M1 or proximal M2 segment; underwent mechanical thrombectomy or intraarterial thrombolysis, with or without IV-tPA before intervention; had follow up MRI obtained within 24 hours to 7 days post endovascular treatment.  The collateral status of patients was defined using a grading system designed by Miteff et al.1 There are 3 grades which include: “good,” where the entire MCA distal to the occluded segment reconstitutes with contrast; “moderate,” where some MCA branches distal to the occluded segment reconstituted in the sylvian fissure; and “poor,” where only distal superficial MCA branches reconstituted distal to the occlusion. Though many different grading systems for collateralization have been created, Al-Dasuqi et al. used the grading system by Miteff et al. because this grading system showed to be reliable in predicting favorable and poor outcomes in patients treated with IV-tPA while other collateral grading systems were of limited value. Successful recanalization was defined by mTICI score of 2b-3. A summation map of all infarct lesions detected on MRI was created to identify regions of infarct associated with mTICI scores and collateral grading. Early functional outcome was measured using the modified Rankin Scale (mRS) at discharge with a favorable outcome defined as mRS score of 0 to 2.

Multimodal Stroke CT in the COVID-19 Era: More With Less

Elena Zapata-Arriaza, MD

Esenwa C, Lee J-A, Nisar T, Shmukler A, Goldman I, Zampolin R, Hsu K, Labovitz D, Altschul D, Haramati LB. Utility of Apical Lung Assessment on Computed Tomography Angiography as a COVID-19 Screen in Acute Stroke. Stroke. 2020.

Acute ischemic stroke (AIS) management has changed since the beginning of the COVID-19 pandemic. Chart flows and assessment protocols have evolved with the aim of redirecting stroke and COVID-19 patients to places prepared for their management. The use of thorax CT has been implemented in patients with ischemic stroke, to identify patients infected with SARS-Cov-2, regardless of respiratory symptoms.

At the beginning of 2020, it was difficult for a vascular neurologist to imagine how essential it is to perform an accurate thoracic imaging test in those patients with ischemic stroke. Although these measures have improved patient management circuits, they have also led to an increase in the time to revascularization treatments with the impact that this entails. Taking advantage of the CT angiography protocols performed in stroke codes, evaluating the diagnostic accuracy of apical lung exam to identify patients with COVID-19, has been the authors’ aim.

Author Interview: Prof. Marc Ribo on “Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography”

Prof. Marc Ribo
Prof. Marc Ribo

An interview with Prof. Marc Ribo, MD, PhD, Assistant Professor at the Stroke Unit/Department of Neurology at the Hospital Vall d’Hebron, Barcelona, Spain.

Interviewed by Dr. Vera Sharashidze, MD, Vascular Neurology Fellow, University of Miami.

They will be discussing the article “Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography,” published in the October 2020 issue of Stroke.

Dr. Sharashidze: First of all, thank you for taking time to discuss this very interesting article. What led you to become interested in this topic?

Prof. Ribo: My first interest in AI analysis of acute stroke imaging began when I met by coincidence with an expert engineer who wanted to use his skills to help stroke patients.