Key result
A 5-variable clinical risk model predicts paroxysmal AF progression with a ~0.65 C-statistic.
Why the study?
Does a clinical risk prediction model accurately predict AF progression in patients with paroxysmal AF?
Observational (n=612)
Does a clinical risk prediction model accurately predict AF progression in patients with paroxysmal AF?
Effect estimate: C-statistic 0.647 (95% CI 0.590-0.707)
A clinical risk prediction model using five baseline variables showed moderate discriminative ability (C-statistic 0.647) for predicting AF progression in patients with paroxysmal AF over 3.4 years.
Model's modest discrimination cautions against routine clinical use for AF progression; leaves open need for refined predictors in paroxysmal AF.
Background: Atrial Fibrillation (AF) may progress from paroxysmal AF (PAF) to more sustained forms, but predicting which patients progress remains a challenge. The RACE V study is a prospective, observational study aiming to characterize phenotypical differences between patients with and without AF progression. Based on interim data of the RACE V study, a clinical risk prediction model for AF progression in patients with PAF was previously developed. The aim of the current analysis is to assess its performance in the complete cohort over extended follow-up. Methods: In the RACE V study, 612 patients with PAF were extensively phenotyped and continuously monitored using implantable loop recorders to track AF recurrences. AF progression was the primary outcome, defined as (1) progression to persistent or permanent AF, or (2) AF burden increase > 3%, during complete follow-up. The risk score incorporates five clinical predictors at baseline: sex, PR interval duration, left atrial contractile function, waist circumference, and presence of mitral valve regurgitation. Prediction model performance was assessed using receiver operating characteristic (ROC) curve-derived area under the curve (AUC). Results: Patients had a median age 64 [57 – 70] years, and 42% were female. During a follow-up of 3.4 (2.8 – 3.7) years, 108 (5.2%/year) patients progressed. The risk prediction model demonstrated a C-statistic of 0.647 (95% CI: 0.590–0.707) in the full cohort, compared with 0.709 (95% CI: 0.617–0.801) in the interim cohort (DeLong's unpaired test, p = 0.656). Conclusions: The model may help clinicians identify patients at risk of progression, showing stable performance in the whole cohort over extended follow-up.
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Baron et al. (2026) conducted an observational in paroxysmal atrial fibrillation (n=612). Clinical risk prediction model was evaluated on AF progression (progression to persistent or permanent AF, or AF burden increase > 3%) (C-statistic 0.647, 95% CI 0.590-0.707). A 5-variable clinical risk prediction model for atrial fibrillation progression in patients with paroxysmal AF demonstrated a C-statistic of 0.647 (95% CI 0.590-0.707) over 3.4 years of follow-up.
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