Prime time for machine learning to predict clinical outcomes in valvular heart disease?
- PMID: 34761488
- DOI: 10.1002/ejhf.2379
Prime time for machine learning to predict clinical outcomes in valvular heart disease?
Comment on
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Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis.Eur J Heart Fail. 2021 Dec;23(12):2021-2032. doi: 10.1002/ejhf.2361. Epub 2021 Oct 21. Eur J Heart Fail. 2021. PMID: 34632675
References
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- Vahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J, et al. 2021 ESC/EACTS guidelines for the management of valvular heart disease. Eur Heart J. 2021. https://doi.org/10.1093/eurheartj/ehab395.
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- White M, Baral R, Ryding A, Tsampasian V, Ravindrarajah T, Garg P, et al. Biomarkers associated with mortality in aortic stenosis: a systematic review and meta-analysis. Med Sci. 2021;9(2):29.
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- Allen CJ, Joseph J, Patterson T, Hammond-Haley M, McConkey HZR, Prendergast BD, et al. Baseline NT-proBNP accurately predicts symptom response to transcatheter aortic valve implantation. J Am Heart Assoc. 2020;9(23):e017574.
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- Spampinato RA, Bochen R, Sieg F, Weiss S, Kornej J, Haunschild J, et al. Multi-biomarker mortality prediction in patients with aortic stenosis undergoing valve replacement. J Cardiol. 2020;76(2):154-62.
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- Woolley RJ, Ceelen D, Ouwerkerk W, Tromp J, Figarska SM, Anker SD, et al. Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. Eur J Heart Fail. 2021;23(6):983-91.
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