Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches

BACKGROUND: Cardiac implantable electronic devices (CIEDs) frequently detect brief, often subclinical atrial fibrillation (AF), but their value for predicting progression to persistent AF remains uncertain. Statistical and machine learning (ML) approaches may enable dynamic risk stratification using this longitudinal device data. OBJECTIVE: To develop a risk stratification model using clinical and CIED-derived AF burden measured over a rolling 6-month window to predict progression to persistent AF. METHODS: We analyzed continuous CIED data from 1985 patients without prior persistent AF implanted between 2016 and 2024 at a tertiary medical center. AF burden and clinical variables were summarized using overlapping 6-month rolling windows to estimate 1-year risk of persistent AF. Associations were evaluated using Kaplan-Meier and Cox proportional hazards models. A gradient-boosted decision tree model (XGBoost) was used to predict progression. RESULTS: During a mean follow-up of 1192 days, 874 patients (44%) developed paroxysmal AF, of whom 257 (29%) progressed to persistent AF after a mean of 813 days. Patients with no AF or < 1 h/day of AF in the prior 6 months had > 97% 1-year freedom from persistent AF, whereas those with > 8 h/day had a 63% progression rate. Higher AF burden was strongly associated with progression (maximum HR 8.66, p < 0.001). The ML model demonstrated high predictive performance (sensitivity 99.4%, specificity 95.7%). CONCLUSION: CIED-detected AF burden is strongly associated with progression to persistent AF. ML-based analysis of 6-month device data enables accurate, point-in-time risk stratification to support earlier and more targeted clinical management.