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

Abstract
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.
In pooled European community cohorts (
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

Stephanie Brandt
Author

Jan Brederecke
Author

Francesco Gianfagna
Author

Julie K K Vishram-Nielsen
Author

Francisco M Ojeda
Author

Simona Costanzo
Author

Christin S Börschel
Author

Stefan Söderberg
Author

Ioannis Katsoularis
Author

Erkki Vartiainen
Author

Maria Benedetta Donati
Author

Jukka Kontto
Author

Martin Bobak
Author

Ellisiv B Mathiesen
Author

Allan Linneberg
Author

Maja-Lisa Løchen
Author

Augusto Di Castelnuovo
Author

Stefan Blankenberg
Author

Giovanni de Gaetano
Author

Kari Kuulasmaa
Author

Licia Iacoviello
Author

Tanja Zeller
Author








