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Promising Biomarker Candidates for Cardioembolic Stroke Etiology. A Brief Narrative Review and Current Opinion.

ABSTRACT: Determining the cause of stroke is considered one of the main objectives in evaluating a stroke patient in clinical practice. However, ischemic stroke is a heterogeneous disorder and numerous underlying disorders are implicated in its pathogenesis. Although progress has been made in identifying individual stroke etiology, in many cases underlying mechanisms still remain elusive. Since secondary prevention strategies are tailored toward individual stroke mechanisms, patients whose stroke etiology is unknown may not receive optimal preventive treatment. Cardioembolic stroke is commonly defined as cerebral vessel occlusion by distant embolization arising from thrombus formation in the heart. It accounts for the main proportion of ischemic strokes, and its share to stroke etiology is likely to rise even further in future decades. However, it can be challenging to distinguish cardioembolism from other possible etiologies. As personalized medicine advances, stroke researchers' focus is increasingly drawn to etiology-associated biomarkers. They can provide deeper insight regarding specific stroke mechanisms and can help to unravel previously undetected pathologies. Furthermore, etiology-associated biomarkers could play an important role in guiding future stroke prevention strategies. To achieve this, broad validation of promising candidate biomarkers as well as their implementation in well-designed randomized clinical trials is necessary. This review focuses on the most-promising candidates for diagnosis of cardioembolic stroke. It discusses existing evidence for possible clinical applications of these biomarkers, addresses current challenges, and outlines future perspectives.

SUBMITTER: Markus A

PROVIDER: S-EPMC7947187 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Promising Biomarker Candidates for Cardioembolic Stroke Etiology. A Brief Narrative Review and Current Opinion.

Markus Arnold A Valerie Schütz S Mira Katan K

Frontiers in neurology 20210225

Determining the cause of stroke is considered one of the main objectives in evaluating a stroke patient in clinical practice. However, ischemic stroke is a heterogeneous disorder and numerous underlying disorders are implicated in its pathogenesis. Although progress has been made in identifying individual stroke etiology, in many cases underlying mechanisms still remain elusive. Since secondary prevention strategies are tailored toward individual stroke mechanisms, patients whose stroke etiology ...[more]

PMID: 33716927

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Project description:Background and purposeOral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database.MethodsWe adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes.ResultsRegular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features.ConclusionsMachine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

Dataset Information

Promising Biomarker Candidates for Cardioembolic Stroke Etiology. A Brief Narrative Review and Current Opinion.

Publications

Promising Biomarker Candidates for Cardioembolic Stroke Etiology. A Brief Narrative Review and Current Opinion.

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