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. Issue 3 (4th January 2023)

• Record Type: Journal Article
• Authors: Toprak, Betül; Brandt, Stephanie; Brederecke, Jan; Gianfagna, Francesco; Vishram-Nielsen, Julie K K; Ojeda, Francisco M; Costanzo, Simona; Börschel, Christin S; Söderberg, Stefan; Katsoularis, Ioannis; Camen, Stephan; Vartiainen, Erkki; Donati, Maria Benedetta; Kontto, Jukka; Bobak, Martin; Mathiesen, Ellisiv B; Linneberg, Allan; Koenig, Wolfgang; Løchen, Maja-Lisa; Di Castelnuovo, Augusto; Blankenberg, Stefan; de Gaetano, Giovanni; Kuulasmaa, Kari; Salomaa, Veikko; Iacoviello, Licia; Niiranen, Teemu; Zeller, Tanja; Schnabel, Renate B
• Is Part Of: Europace. Volume 25:Issue 3(2023)
• Page Start: 812
• Page End: 819
• Publication Date: 2023-01-04
• Subjects: Atrial fibrillation -- Biomarkers -- Risk Prediction -- Machine learning -- Epidemiology -- Community; Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euac260
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Aims: 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. Graphical Abstract: Graphical Abstract AMDMS, Averaged minimal depth of a maximal subtree; BMI, body mass index; BP, blood pressure; CRP, C-reactive protein; HDL, high-density lipoprotein; HF, heart failure; LASSO, Least absolute shrinkage and selection operator; LDL, low-density lipoprotein; LOD, limit of detection; MI, myocardial infarction; NT-proBNP, N-terminal pro B-type natriuretic peptide; RSF, Random survival forest; VIMP, variable importance.