PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 2, 20260 citationsOpen Access

Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation

PBPascal Braun-MeyreSAStefanie AeschbacherSBSteffen Blum

Key Result

A panel of 5 circulating biomarkers independently predicted cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy in AF patients.

Key Points

  • The central aim is to evaluate biomarker panels for improving risk prediction of cardiovascular events in patients with atrial fibrillation.
  • Evaluated a panel of 12 circulating biomarkers in 3817 atrial fibrillation patients.
  • Identified association of biomarkers with adverse cardiovascular outcomes.
  • Utilized machine learning models to enhance risk stratification.
  • Five biomarkers were found to independently predict cardiovascular death and stroke.
  • GDF-15 and other biomarkers predicted heart failure hospitalization and bleeding events.
  • Biomarker models improved predictive accuracy for stroke and bleeding compared to clinical risk scores.

Study Design

Type

Observational (n=3,817)

Structured PICO

Does a panel of circulating biomarkers improve risk prediction for adverse cardiovascular outcomes in patients with atrial fibrillation compared to established clinical risk scores?

P
Population
3,817 patients with atrial fibrillation evaluated for the association of circulating biomarkers with adverse cardiovascular outcomes.
E
Exposure
Biomarker-based risk prediction models incorporating a panel of 12 circulating biomarkers (including D-dimer, GDF-15, IL-6, NT-proBNP, hsTropT, and IGFBP-7)
C
Comparator
Established clinical risk scores
O
Outcome
Adverse cardiovascular outcomes (cardiovascular death, stroke, myocardial infarction, and systemic embolism)hard clinical

Integrating a panel of circulating biomarkers into conventional and machine learning models improves risk stratification for adverse cardiovascular and bleeding events in patients with atrial fibrillation.

Abstract

Atrial fibrillation (AF) increases the risk of adverse cardiovascular events, yet the underlying biological mechanisms remain unclear. We evaluate a panel of 12 circulating biomarkers representing diverse pathophysiological pathways in 3817 AF patients to assess their association with adverse cardiovascular outcomes. We identify 5 biomarkers including 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 independently predict 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 predict heart failure hospitalization, while GDF-15 and IL-6 are associated with major bleeding events. A biomarker model improves predictive accuracy for stroke and major bleeding compared to established clinical risk scores. Machine learning models incorporating these biomarkers demonstrate consistent improvements in risk stratification across most outcomes. In this work, we show that 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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Braun-Meyre et al. (2025) conducted an observational in Atrial fibrillation (n=3,817). Circulating biomarker panels vs. Established clinical risk scores was evaluated on Cardiovascular death, stroke, myocardial infarction, and systemic embolism. A panel of 5 circulating biomarkers independently predicted cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy in AF patients.

synapsesocial.com/papers/6980fecbc1c9540dea81122fhttps://doi.org/10.5167/uzh-284116
Ask AI
Helpful
Bookmark
Share
View Full Paper