ArticleCardiology research2025
Computer-Assisted Electrocardiogram Analysis Improves Risk Assessment of Underlying Atrial Fibrillation in Cryptogenic Stroke.
Article in Cardiology research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of Left Atrial Echocardiographic Indices With Atrial Fibrillation in Cryptogenic Stroke: A Systematic Review and Meta-Analysis.Echocardiography (Mount Kisco, N.Y.) · 2026Pooled it
- Ultra-Early Continuous ECG Monitoring for Silent Atrial Fibrillation Diagnosis in Cryptogenic Stroke: The CRIPTOFAST Randomized Controlled Trial.European journal of neurology · 2026Trial
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS. Methods: Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF. Results: The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's D Conclusions: The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.
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