Nguyen BO, Weberndorfer V, Crijns HJ, Geelhoed B, Ten Cate H, Spronk H, Kroon A, De With R, Al-Jazairi M, Maass AH, Blaauw Y, Tieleman RG, Hemels MEW, Luermans J, de Groot J, Allaart CP, Elvan A, De Melis M, Scheerder C, van Zonneveld AJ, Schotten U, Linz D, Van Gelder I, Rienstra M.
Heart. 2022 Jul 20:heartjnl-2022-321027. PMID: 35858774
Objective: Atrial fibrillation (AF) often progresses from paroxysmal AF (PAF) to more permanent forms. To improve personalised medicine, we aim to develop a new AF progression risk prediction model in patients with PAF.
Methods: In this interim-analysis of the Reappraisal of AF: Interaction Between HyperCoagulability, Electrical Remodelling, and Vascular Destabilisation in the Progression of AF study, patients with PAF undergoing extensive phenotyping at baseline and continuous rhythm monitoring during follow-up of ≥1 year were analysed. AF progression was defined as (1) progression to persistent or permanent AF or (2) progression of PAF with >3% burden increase. Multivariable analysis was done to identify predictors of AF progression.
Results: Mean age was 65 (58-71) years, 179 (43%) were female. Follow-up was 2.2 (1.6-2.8) years, 51 of 417 patients (5.5%/year) showed AF progression. Multivariable analysis identified, PR interval, impaired left atrial function, mitral valve regurgitation and waist circumference to be associated with AF progression. Adding blood biomarkers improved the model (C-statistic from 0.709 to 0.830) and showed male sex, lower levels of factor XIIa:C1-esterase inhibitor and tissue factor pathway inhibitor, and higher levels of N-terminal pro-brain natriuretic peptide, proprotein convertase subtilisin/kexin type 9 and peptidoglycan recognition protein 1 were associated with AF progression.
Conclusion: In patients with PAF, AF progression occurred in 5.5%/year. Predictors for progression included markers for atrial remodelling, sex, mitral valve regurgitation, waist circumference and biomarkers associated with coagulation, inflammation, cardiomyocyte stretch and atherosclerosis. These prediction models may help to determine risk of AF progression and treatment targets, but validation is needed.