Key result
Computer-assisted V1 R wave > 137 µV predicts ~33-fold higher AF odds in cryptogenic stroke.
Why the study?
Detecting underlying paroxysmal AF in cryptogenic stroke is challenging, and the value of computer-assisted 12-lead ECG analysis in identifying predictors of AF was unknown.
Does computer-assisted ECG analysis improve the prediction of underlying atrial fibrillation in patients with cryptogenic stroke?
Cohort (n=67)
Blinded outcome assessment
No
Does computer-assisted ECG analysis improve the prediction of underlying atrial fibrillation in patients with cryptogenic stroke?
Effect estimate: OR 33.04 (95% CI 3.38-1167.86)
p-value: p=0.013
The addition of computer-assisted ECG analysis, specifically the amplitude of the R wave in V1, significantly improves the predictive accuracy for underlying atrial fibrillation in patients with cryptogenic stroke.
May refine AF prediction models in cryptogenic stroke; hypothesis-generating pending prospective validation.
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 Dxy: 0.907, Brier’s B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer’s Dxy: 0.827, Brier’s B: 0.119, AUC: 0.896). 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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Viliani et al. (2025) conducted a cohort in Cryptogenic stroke (n=67). Computer-assisted ECG analysis (V1_ramp > 137 µV) vs. Clinical, biochemical, and echocardiographic model without ECG was evaluated on Detection of atrial fibrillation (OR 33.04, 95% CI 3.38-1167.86, p=0.013). Computer-assisted ECG analysis, specifically an R wave amplitude in V1 > 137 µV, significantly predicted underlying atrial fibrillation in patients with cryptogenic stroke (OR 33.04) and improved the predictive capacity of clinical models.
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