Key result
AI predicts paroxysmal AF from single-lead sinus rhythm ECG with an overall AUC of ~0.66.
Why the study?
AF screening detection yields can be low due to the paroxysmal nature of the disease, and prolonged monitoring can be cumbersome and expensive.
Can an artificial intelligence-based convolutional neural network accurately predict paroxysmal atrial fibrillation from a single-lead normal sinus rhythm ECG in older adults undergoing AF screening?
Cohort (n=14,831)
Yes
Can an artificial intelligence-based convolutional neural network accurately predict paroxysmal atrial fibrillation from a single-lead normal sinus rhythm ECG in older adults undergoing AF screening?
Answers are grounded in this paper and cite their sources.
An AI algorithm applied to a single-lead sinus rhythm ECG can moderately predict undetected paroxysmal AF—especially in age-diverse elderly populations—potentially serving as a low-cost triage step to select candidates for prolonged AF screening.
AIMS: Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. METHODS AND RESULTS: A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively. CONCLUSION: An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.
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Hygrell et al. (2023) conducted a cohort in Paroxysmal atrial fibrillation (n=14,831). Convolutional neural network (AI) analysis of single-lead sinus rhythm ECG vs. Confirmed paroxysmal atrial fibrillation diagnosis via intermittent ECG screening was evaluated on Prediction of paroxysmal atrial fibrillation from a normal sinus rhythm single-lead ECG (measured by AUC) (AUC 0.66 (combined test set); AUC 0.80 (SAFER); AUC 0.62 (STROKESTOP I and II), 95% CI 0.64-0.67). An AI-based convolutional neural network predicted paroxysmal AF from a single-lead sinus rhythm ECG with an AUC of 0.80 in an age-diverse cohort (SAFER) and 0.62 in age-homogenous cohorts.
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