Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning.

Journal: Cardiovascular research
PMID: 33576412

Abstract

AIMS: Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine learning on DVA in order to improve CV mortality prediction and identify corresponding response alterations.

Authors

  • Stanislas Werfel
    Department of Nephrology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany.
  • Roman Günthner
    Department of Nephrology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany.
  • Alexander Hapfelmeier
    Institute of Medical Informatics, Statistics and Epidemiology, School of Medicine, Technical University Munich, Munich, Germany.
  • Henner Hanssen
    Division of Sports Medicine, Institute of Exercise and Health Sciences (ISSW), University of Basel, Basel, Switzerland.
  • Konstantin Kotliar
    Department of Biomedical Engineering and Technomathematics, Aachen University of Applied Sciences, Juelich, Germany.
  • Uwe Heemann
    Department of Nephrology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany.
  • Christoph Schmaderer
    Department of Nephrology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany.

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