Improved risk assessment in parvovirus B19-positive patients with heart failure by multiparametric analysis of endomyocardial biopsy using machine learning methods.

Journal: European journal of heart failure
Published Date: Jul 28, 2025

Abstract

AIMS: The analysis of endomyocardial biopsies (EMB) is a prerequisite for a definitive diagnosis in patients with unexplained heart failure (HF). The use of machine learning (ML) methods may help to identify high-risk patients and to initiate therapy. In this study, we develop ML models for risk stratification of parvovirus B19 (B19V) positive patients with HF based on key features from multiparametric EMB analyses.

Authors

  • Christian Baumeier
    Institute of Cardiac Diagnostics and Therapy, IKDT GmbH, Berlin, Germany.
  • Johannes Starlinger
    Howto Health GmbH, Berlin, Germany.
  • Ganna Aleshcheva
    Institute of Cardiac Diagnostics and Therapy, IKDT GmbH, Berlin, Germany.
  • Gordon Wiegleb
    Institute of Cardiac Diagnostics and Therapy, IKDT GmbH, Berlin, Germany.
  • Felicitas Escher
    Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité, Berlin, Germany.
  • Philip Wenzel
    University Medical Center Mainz, Mainz, Germany.
  • Amin Polzin
    Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
  • Heinz-Peter Schultheiss
    Institute of Cardiac Diagnostics and Therapy, IKDT GmbH, Berlin, Germany.

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