Key result
Adding a biomarker panel improves CV risk prediction AUC to 0.77 in AF patients.
Why the study?
AF is associated with increased adverse cardiovascular risk, but the underlying biological mechanisms remain incompletely understood.
Does the integration of a 12-biomarker panel improve risk prediction for adverse cardiovascular events and bleeding in patients with atrial fibrillation?
Cohort (n=3,817)
Yes
Does the integration of a 12-biomarker panel improve risk prediction for adverse cardiovascular events and bleeding in patients with atrial fibrillation?
Absolute Event Rate: 0.77% vs 0.74%
p-value: p=2.6x10-8
Integrating a panel of circulating biomarkers into traditional and machine learning models significantly improves risk prediction for adverse cardiovascular events and bleeding in patients with atrial fibrillation.
May aid AF risk stratification; leaves open prospective validation before practice change.
Atrial fibrillation (AF) is associated with an increased risk of adverse cardiovascular events, but the underlying biological mechanisms remain incompletely understood. Here we evaluated a panel of 12 circulating biomarkers representing diverse pathophysiological pathways in a cohort of 3,817 AF patients to assess their association with adverse cardiovascular outcomes. We identified 5 biomarkers—d-dimer, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTropT)—that were independently associated with cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy. Additionally, GDF-15, insulin-like growth factor-binding protein-7 (IGFBP-7), NT-proBNP, and hsTropT were strong predictors of heart failure hospitalization, while GDF-15 and IL-6 were associated with major bleeding events. Incorporating IL-6, NT-proBNP, and hsTropT to the CHA₂DS₂-VASc score improved stroke risk prediction. Machine learning models incorporating these biomarkers demonstrated consistent improvements in risk stratification across all outcomes. Our results highlight the potential of integrating biomarkers related to myocardial injury, inflammation, oxidative stress, and coagulation into both conventional and machine learning-based models refine prognosis and guide clinical decision-making in AF patients.
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Meyre et al. (2024) conducted a cohort in Atrial fibrillation (n=3,817). Biomarker panel addition to clinical risk models vs. Clinical risk models alone was evaluated on Composite cardiovascular outcome risk prediction (AUC) (95% CI 0.75-0.79, p=2.6x10-8). Adding a biomarker panel to a clinical model significantly improved risk prediction for composite cardiovascular events, increasing the AUC from 0.74 to 0.77 in patients with atrial fibrillation.
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