ArticleFrontiers in neurology2022
Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Cardioembolic sources and stroke prevention: a systematic review.Cardiovascular ultrasound · 2026Pooled it
- A Prospective Study on Clinical Profile and the Utility of 72-h Holter Monitoring in Acute Ischaemic Stroke and Transient Ischaemic Attack (TIA) Patients.Annals of neurosciences · 2026Article
- Early intensive therapy for preventing neurological deterioration in branch atheromatous disease.Therapeutic advances in neurological disorders · 2025Article
- How Topographic Diffusion-Weighted Imaging Patterns can Predict the Potential Embolic Source.Clinical neuroradiology · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: The early identification of cardioembolic stroke is critical for the early initiation of anticoagulant treatment. However, it can be challenging to identify the major cardiac source, particularly since the predominant source, paroxysmal atrial fibrillation (AF), may not be present at the time of stroke. In this study, we aimed to evaluate imaging predictors for unrecognized AF in patients with acute ischemic stroke. Methods: We performed a cross-sectional analysis of data and magnetic resonance imaging (MRI) scans from two prospective cohorts of patients who underwent serial 12-lead electrocardiography and 24-h Holter monitoring to detect unrecognized AF. The imaging patterns in diffusion-weighted imaging and imaging characteristics were assessed and classified. A logistic regression model was used to identify predictive factors for newly detected AF in patients with acute ischemic stroke. Results: A total of 734 patients were recruited for analysis, with a median age of 72 (interquartile range: 65-79) years and a median National Institutes of Health Stroke Scale score of 4 (interquartile range: 2-6). Of these patients, 64 (8.7%) had newly detected AF during the follow-up period. Stepwise multivariate logistic regression revealed that age ≥75 years [adjusted odds ratio (aOR) 5.66, 95% confidence interval (CI) 2.98-10.75], receiving recombinant tissue plasminogen activator treatment (aOR 4.36, 95% CI 1.65-11.54), congestive heart failure (aOR 6.73, 95% CI 1.85-24.48), early hemorrhage in MRI (aOR 3.62, 95% CI 1.52-8.61), single cortical infarct (aOR 6.49, 95% CI 2.35-17.92), and territorial infarcts (aOR 3.54, 95% CI 1.06-11.75) were associated with newly detected AF. The C-statistic of the prediction model for newly detected AF was 0.764. Conclusion: Initial MRI at the time of stroke may be useful to predict which patients have cardioembolic stroke caused by unrecognized AF. Further studies are warranted to verify these findings and their application to high-risk patients.
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