Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods

EP Europace Journal

4 January 2023
Organised by: Logo
ESC Journals ARRHYTHMIAS AND DEVICE THERAPY Research Methodology Atrial Fibrillation (AF) PREVENTIVE CARDIOLOGY Risk Factors and Prevention

Abstract

AbstractAims

To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables.

Methods and results

In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. In multivariable-adjusted Cox-regression analysis, NT-proBNP was the strongest circulating predictor of incident AF [hazard ratio (HR) per standard deviation (SD), 1.93 (95% CI, 1.82–2.04); P < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13–1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10–1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02–1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index.

Conclusion

Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively.

Contributors

Betül Toprak
Betül Toprak

Author

University Heart and Vascular Centre Hamburg (UHZ) Hamburg , Germany

Stephan Camen
Stephan Camen

Author

University Heart and Vascular Centre Hamburg (UHZ) Hamburg , Germany

Wolfgang Koenig
Wolfgang Koenig

Author

TUM Universitätsklinikum, German Heart Center Munich , Germany

Veikko Salomaa
Veikko Salomaa

Author

National Institute for Health and Welfare (THL) Helsinki , Finland

Teemu Niiranen
Teemu Niiranen

Author

University of Turku Turku , Finland

Renate B Schnabel
Renate B Schnabel

Author

University Heart and Vascular Centre Hamburg (UHZ) Hamburg , Germany

ESC 365 is supported by