Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis.

Journal: Clinical nutrition ESPEN
Published Date: Jun 17, 2024

Abstract

BACKGROUND & AIMS: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans.

Authors

  • Dennis van Erck
    Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. Electronic address: d.vanerck@amsterdamumc.nl.
  • Pim Moeskops
  • Josje D Schoufour
    Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Science, Tafelbergweg 51, 1105 BD, Amsterdam, The Netherlands; Center of Expertise Urban Vitality, Faculty of Sports and Nutrition, Amsterdam University of Applied Sciences, Dokter Meurerlaan 8, 1067 SM, Amsterdam, The Netherlands.
  • Peter J M Weijs
    Center of Expertise Urban Vitality, Faculty of Sports and Nutrition, Amsterdam University of Applied Sciences, Dokter Meurerlaan 8, 1067 SM, Amsterdam, The Netherlands.
  • Wilma J M Scholte Op Reimer
    Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Research Group Chronic Diseases, HU University of Applied Sciences, Heidelberglaan 15, 3584 CS, Utrecht, The Netherlands.
  • Martijn S van Mourik
    Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
  • R Nils Planken
    Departments of Radiology and Nuclear Medicine (C.P.S.B., A.J.N., P.v.O., R.N.P.) and Cardiology (S.M.B.), Amsterdam University Medical Centers, Location Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.J.M.W.); Department of Research and Development, Pie Medical Imaging BV, Maastricht, the Netherlands (J.P.A.); and Departments of Cardiology (G.P.B., S.A.J.C.) and Radiology (T.L.), University Medical Center Utrecht, Utrecht, the Netherlands.
  • Marije M Vis
    Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
  • Jan Baan
    Department of Cardiology, Amsterdam UMC, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands. Electronic address: j.baan@amsterdamumc.nl.
  • Ivana Išgum
    Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.
  • José P Henriques
    Heart Centre, Academic Medical Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands. Electronic address: j.p.henriques@amsterdamumc.nl.
  • Bob D de Vos
    Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. Electronic address: b.d.devos-2@umcutrecht.nl.
  • Ronak Delewi
    Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.

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