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.

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

Gianfagna, Francesco;Iacoviello, Licia;
2023-01-01

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.
2023
Atrial fibrillation; Biomarkers; Community; Epidemiology; Machine learning; Risk Prediction
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
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2145855
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