PPG-based smartwatch algorithm detected AF with 99.2% accuracy and estimated AF burden with near-perfect correlation (r=0.999) to ECG patch in 728 AF patients.
Does a continuous PPG-based smartwatch algorithm accurately estimate atrial fibrillation burden compared to a single-lead ECG patch in patients with atrial fibrillation?
A continuous PPG-based smartwatch algorithm provides highly accurate and consistent atrial fibrillation burden monitoring compared to a reference single-lead ECG patch.
Tasa de eventos absoluta: 0% vs 0%
Abstract Background Wearable devices utilizing photoplethysmography(PPG)-based algorithm smartwatch can effectively monitor atrial fibrillation (AF). However, evidence of continuous PPG-based smartwatch for AF burden monitoring is rather limitied. Purpose This study aims to evaluate the accuracy and consistency of a continous PPG-based smartwatch algorithm for AF burden monitoring compared to a single-lead ECG patch in a large sample of Chinese patients with AF. Methods In an observational study, we enrolled AF patients at a single site centre from January to April 2024 to evaluate the performance of PPG and a PPG-based algorithm in estimating AF burden. Using an single-lead ECG patch as the reference device, we validated the algorithm’s ability to detect AF and estimate AF burden in 30-second intervals. Results The study included 728 patients with AF (67.3% male，median age of 62.0 years). The average monitoring duration was 20.4 ± 4.5 hours. After dividing the monitoring time into non-overlapping 30-second intervals, 1,440,826 paired ECG and PPG recordings were generated. The overall valid recording rates were 96.23% for ECG and 62.50% for PPG. At the patient level, PPG demonstrated an accuracy of 99.2%, sensitivity of 98.6%, and specificity of 99.5%. At the segment level, these metrics were 94.0%, 92.0%, and 96.4%, respectively. Across different time periods, PPG’s accuracy, sensitivity, and specificity all exceeded 98%. For patients with at least one AF episode, the AF burden estimated by wrist-based PPG (W-PPG) strongly correlated with that calculated by patch-based ECG (P-ECG) (r = 0.999). Bland-Altman analysis revealed high agreement between PPG and ECG in AF burden estimation, with a mean difference of -0.335% and 95% limits of agreement ranging from -5.333% to 4.662%. Consistent results were observed across subgroups, with most measurements (96.4%) falling within the 95% confidence interval, indicating excellent agreement. Conclusion Our findings demonstrate that smartwatches equipped with PPG-based algorithms exhibit high accuracy and stability in continuously monitoring AF burden compared to ECG patches, highlighting their potential for AF diagnosis and management.
Kong et al. (Sat,) reported a other. PPG-based smartwatch algorithm detected AF with 99.2% accuracy and estimated AF burden with near-perfect correlation (r=0.999) to ECG patch in 728 AF patients.
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