Smartphone Application-Derived Clusters of Persistent Symptoms in Patients After Atrial Fibrillation Ablation: Data...

Atrial fibrillation (AF) is characterized by a heterogeneous presentation of symptoms. AF ablation reduces symptom burden. However, persistent symptoms following AF ablation are common independently of AF recurrence.

This study sought to perform a cluster analysis to identify clinically relevant AF subphenotypes based on persistent symptoms following AF ablation and evaluate their associations with clinical characteristics and AF recurrence.

Patients were instructed to perform smartphone app-based simultaneous symptom and photoplethysmography heart rhythm monitoring 3 times daily for 1 week at the 3-month follow-up after AF ablation. A two-step cluster analysis including 7 categorical symptoms variables was performed in symptomatic patients.

In total, half of all patients (n = 313 of 614 [51%]) reported symptoms. Five symptom clusters were identified: nonspecified symptoms (n = 52 [17%]), AF with sparse symptoms (n = 93 [30%]), palpitations (n = 47 [15%]), fatigue with comorbidities (n = 63 [20%]), and sinus rhythm with severe symptoms (n = 58 [19%]). Frequency (P < 0.001) and pattern (P < 0.001) of symptom reporting as well as AF recurrence (P < 0.001), AF load (P < 0.001), AF pattern (P = 0.002 and P = 0.005), and symptom-rhythm correlation (P < 0.001) differed between clusters. Furthermore, age (P < 0.01), N-terminal pro-B-type natriuretic peptide levels (P < 0.01), CHA₂DS₂-VA score (P < 0.001), and left atrial volume index (P = 0.01) differed between clusters.

Half of all patients report symptoms after AF ablation. Using cluster analysis, 5 symptom-based AF subphenotypes were identified, each with distinct clinical characteristics, biomarker profiles, AF recurrence, AF pattern, AF and symptom burden, and symptom-rhythm correlation. Symptom clusters empowered by digital health may facilitate individualized AF management strategies following AF ablation.