Beyond burden metrics: Wearable photoplethysmography-derived spatiotemporal progression of atrial fibrillation linked to clinical outcomes.

BACKGROUND

Traditional atrial fibrillation (AF) classification lacks dynamic quantification. Current AF burden assessment is constrained by intermittent monitoring and simplistic metrics.

OBJECTIVE

This study aimed to establish a continuous, multidimensional AF progression model using wearable photoplethysmography (PPG) for real-world, dynamic burden quantification.

METHODS

In this prospective cohort, 110 patients with paroxysmal AF underwent synchronized PPG (Huawei Watch GT3) and 24-hour Holter monitoring. We developed a multiscale fusion AF algorithm and a 5-dimensional spatiotemporal progression model quantifying episode frequency, duration, clustering, circadian rhythm, and tachycardia burden.

RESULTS

The fusion algorithm achieved an accuracy of 0.97. The 5-dimensional model showed strong concordance with Holter monitoring, with near-perfect correlation for episode duration (r = 0.97) and high interchangeability (intraclass correlation coefficient >0.75). It demonstrated excellent diagnostic performance for burden trajectories (area under the curve 0.98). A composite AF burden score of ≥0.59 identified patients at high risk of AF-related symptoms or heart rate issues. Clinically, AF burden increased with worsening European Heart Rhythm Association symptoms (P = .04), varied by risk profile (P = .03), and differed between ablation- and drug-treated patients (episode duration P < .001).

CONCLUSION

Continuous PPG-based spatiotemporal modeling robustly quantified dynamic AF progression, enabled precise phenotyping, and may support early intervention in high-risk patients.