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Inflammatory biomarkers in infective endocarditis: machine learning to predict mortality.

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New biomarkers are needed for infective endocarditis (IE) mortality. Interleukin-15 (IL-15) and C-C motif chemokine ligand (CCL4) predict death, improving risk stratification beyond C-reactive protein (CRP).

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Area of Science:

  • Cardiology
  • Immunology
  • Biomarker Discovery

Background:

  • Infective endocarditis (IE) has high mortality rates.
  • Improved patient management requires novel biomarkers for risk stratification.
  • Current prognostic tools may not fully capture IE patient risk.

Purpose of the Study:

  • To investigate if cytokines, chemokines, and growth factors at diagnosis predict mortality in IE patients.
  • To identify novel biomarkers that enhance risk prediction beyond C-reactive protein (CRP).
  • To develop a predictive model for IE patient outcomes using machine learning.

Main Methods:

  • Analysis of 27 cytokines, chemokines, and growth factors using Luminex assay in 69 IE patients.
  • Application of machine learning techniques to predict mortality.
  • Development of a decision tree incorporating biomarker levels and CRP for risk stratification.

Main Results:

  • In-hospital mortality was 26%.
  • Interleukin-15 (IL-15) and C-C motif chemokine ligand (CCL4) significantly predicted death.
  • A decision tree model achieved 91% accuracy in outcome prediction.
  • High-risk group (elevated CRP, IL-15, CCL4) had 88% mortality; low-risk had 8% mortality.

Conclusions:

  • Cytokines IL-15 and CCL4 are valuable predictors of mortality in IE.
  • These biomarkers offer prognostic value beyond CRP levels.
  • Assessing cytokines holds potential for clinical risk stratification and monitoring of IE patients.