Impact of biological age versus chronological age on clinical outcomes in patients with atrial fibrillation: insights from the COOL-AF registry

Abstract

Because patients with atrial fibrillation (AF) often exhibit heterogeneous risks that are not fully captured by traditional clinical factors, identifying a more accurate measure of physiological ageing could improve risk stratification and clinical management compared to chronological aging.This study aimed to determine the clinical outcome in relation to biological ageing in patients with AF. We used the data from the COOL-AF registry which is a multicentre nationwide registry of AF patients. The enrolment period was 2014–2017. Patients were followed-up for 3 years. Biological ageing was calculated from the Klemera-Doubal method (KDM) is a based on chronological age and blood chemistry and body function factors. The main outcome of this study was the composite of all-cause death, major bleeding, ischemic stroke/systemic embolism (SSE), and heart failure. We included total of 3405 patients, with a mean chronological age of 67.8 ± 11.3 years, and 1424 (41.8%) were female. During the median follow-up duration of 35.9 (IQR 34.8, 36.0) months, the composite outcomes, death, major bleeding, SSE, and heart failure developed in 726 (21.3%), 380 (11.2%), 199 (5.8%), 134 (3.9%), and 247 (7.3%) patients, respectively. Restricted cubic spline analysis showed that KDM bioage had higher hazard ratios compared to chronological age, with the adjusted Hazard ratios and 95% confidence interval (CI) of Quartile 4 (Q4) KDM for the composite outcomes, death, major bleeding, SSE, and heart failure were 2.11 (1.82–2.45), 2.53 (2.06–3.11), 2.12 (1.60–2.83), 1.96 (1.37–2.78), and 1.83 (1.42–2.38), respectively (all p < 0.001). In conclusion, KDM bioage is an independent predictor for clinical outcome and performs better than chronological age. These findings highlight the clinical value of incorporating biological ageing metrics into AF risk assessment models and suggest that KDM bioage may enhance personalized prognostication beyond conventional age-based evaluation.