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Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis

Mahesh K Vidula1, Alena Orlenko2, Lei Zhao3

  • 1Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.

European Journal of Heart Failure
|October 11, 2021
PubMed

Insights

Plasma biomarkers, particularly those indicating inflammation and calcification, effectively predict adverse outcomes in aortic stenosis (AS) patients. Machine learning models integrating these markers improve risk stratification for better patient management.

Area of Science:

  • Cardiology
  • Biomarker Discovery
  • Medical Diagnostics

Background:

  • Enhanced risk stratification is crucial for managing patients with aortic stenosis (AS).
  • Identifying patients at high risk for adverse outcomes, including myocardial damage, is essential for tailored management.
  • Current risk assessment may not fully capture the complexity of AS progression.

Purpose of the Study:

  • To identify specific plasma biomarkers associated with adverse outcomes in patients with AS.
  • To develop multimarker profiles using machine learning for improved risk prediction.
  • To investigate the prognostic value of inflammation and calcification biomarkers in AS.

Main Methods:

  • A cohort of 708 patients with calcific AS was analyzed.
  • 49 plasma biomarkers were measured using a Luminex platform.
  • Machine learning algorithms were employed to develop multimarker models for predicting death and death or heart failure-related hospital admission (DHFA).

Main Results:

  • Multiple biomarkers, including tumor necrosis factor-alpha (TNF-α) and fibroblast growth factor-23 (FGF-23), were significantly predictive of death and DHFA.
  • Machine-learning models integrating multiple biomarkers demonstrated strong associations with adverse outcomes.
  • Interleukin-6 (IL-6) and FGF-23 emerged as the most important biomarkers in the predictive models.

Conclusions:

  • Plasma biomarkers are significantly associated with the risk of adverse outcomes in patients with AS.
  • Biomarkers related to inflammation (e.g., IL-6) and calcification (e.g., FGF-23) are strongly linked to prognosis.
  • These findings support the use of plasma biomarkers for improved risk stratification in AS management.
Abstract

Keywords:
Aortic stenosisBiomarkersCalcificationInflammationMachine learning

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