Why the study?
Paroxysmal AF is a major potential cause of ESUS but remains challenging to identify because it occurs sporadically.
Does a deep learning algorithm using sinus rhythm ECG improve the identification of paroxysmal atrial fibrillation compared to conventional risk models in patients with embolic stroke of undetermined source?
Does a deep learning algorithm using sinus rhythm ECG improve the identification of paroxysmal atrial fibrillation compared to conventional risk models in patients with embolic stroke of undetermined source?
A deep learning algorithm applied to standard sinus rhythm ECGs can accurately identify hidden paroxysmal atrial fibrillation in patients with embolic stroke of undetermined source, outperforming traditional risk scores.
May aid AF detection in ESUS; leaves open clinical adoption pending prospective validation.
BACKGROUND AND OBJECTIVES: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. METHODS: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. RESULTS: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. CONCLUSIONS: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.
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Jeon et al. (2023) studied this question.
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