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Publications

Journal Article Epublish

PubMed: 42881862 DOI: 10.3390/jcm15197400

Artificial Intelligence-Derived Computed Tomography Phenotyping for Cardiovascular Risk Stratification in Transcatheter Mitral Valve Replacement.

J Clin Med 15(19)

Authors: Liliane Zillner, Tillmann Kerbel, Paul Sautner, Mirjam G Wild, Michaela M Hell, Elmar W Kuhn, Tanja K Rudolph, Thomas Walther, Lenard Conradi, Andreas Zierer, Francesco Maisano, Marco Russo, Fabrizio Rosati, Andrea Colli, Miguel Piñón, David Reineke, Gaby Aphram, Christophe Dubois, Jörg Hausleiter, Ralph Stephan von Bardeleben, Iuliana Coti, Christian Loewe, Daniel Zimpfer, Georg Heinze, Martin Andreas

Affiliations

  • Department of Cardiac and Thoracic Aortic Surgery, Medical University of Vienna, 1090 Vienna, Austria.
  • Medizinische Klinik I, LMU University Hospital, 81377 Munich, Germany.
  • Department of Cardiology, University Medical Center Mainz, 55131 Mainz, Germany.
  • Department of Cardiothoracic Surgery, Heart Center, University Hospital Cologne, 50937 Cologne, Germany.
  • Department of Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, 44801 Bad Oeynhausen, Germany.
  • Department of Cardiac Surgery, University Hospital Frankfurt, 60590 Frankfurt, Germany.
  • Department of Cardiac Surgery, Heart Center, University Hospital Cologne, 50937 Cologne, Germany.
  • Department for Cardiac, Vascular and Thoracic Surgery, Kepler University Hospital Linz, 4020 Linz, Austria.
  • Valve Center, IRCCS Ospedale San Raffaele, University Vita-Salute San Raffaele, 20132 Milan, Italy.
  • Department of Cardiac Surgery and Heart Transplantation, Azienda Ospedaliera San Camillo Forlanini, 00152 Rome, Italy.
  • Division of Cardiac Surgery, Spedali Civili di Brescia, University of Brescia, 25121 Brescia, Italy.
  • Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, 56126 Pisa, Italy.
  • Servicio Cirugía Cardíaca, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain.
  • Department of Cardiac Surgery, Inselspital University Hospital Bern, 3010 Bern, Switzerland.
  • Department of Cardiovascular and Thoracic Surgery, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium.
  • Department of Cardiovascular Medicine, University Hospital Leuven, 3000 Leuven, Belgium.
  • Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium.
  • German Centre for Cardiovascular Research, Munich Heart Alliance, 80336 Munich, Germany.
  • Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria.
  • Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, 1090 Vienna, Austria.
  • Division of Cardiac Surgery, Department of Surgery, Medical University Graz, 8010 Graz, Austria.

Abstract

Background: Despite high procedural success of transcatheter mitral valve replacement (TMVR), mortality remains substantial, and conventional risk scores inadequately reflect the specific risk profile of this high-risk population. Artificial intelligence (AI)-derived analysis may enable TMVR-specific risk assessment using routinely acquired computed tomography (CT). Methods: In a retrospective European multicenter registry, AI-based segmentation of harmonized real-world pre-procedural cardiac CT with variable thoracic and upper-abdominal coverage was applied in patients undergoing TMVR with the Tendyne TM system (134 patients; 346 CT-derived variables retained for high-dimensional screening). One-year cardiovascular mortality was assessed using Cox proportional hazards models with Benjamini-Hochberg false discovery rate correction. STS- and EuroSCORE II-adjusted analyses and a targeted coronary-ventricular coupling analysis were performed. Results: In the STS-adjusted Cox analysis, nine AI-derived CT parameters met the 10% FDR discovery threshold for cardiovascular mortality, spanning skeletal (n = 3), muscular (n = 1), and cardiopulmonary phenotypes (n = 5), whereas three parameters met this threshold in CT-only analyses. Coronary-ventricular coupling, defined as coronary artery volume relative to left ventricular end-diastolic volume, was significantly associated with lower 1-year cardiovascular mortality in the CT-only analysis (HR per IQR 0.47, 95% CI 0.22-1.00; p = 0.049) and after EuroSCORE II adjustment (HR 0.47, 95% CI 0.22-0.99; p = 0.046), while the STS-adjusted estimate remained directionally consistent but did not reach statistical significance (HR 0.49, 95% CI 0.23-1.05; p = 0.067). Conclusions: AI-based CT phenotyping identifies imaging-derived markers associated with cardiovascular mortality beyond information captured by conventional surgical risk scores in TMVR patients. Coronary-ventricular coupling emerges as a promising automated CT-derived imaging marker with a biologically plausible pathophysiological basis, linking coronary arterial volume to ventricular size in mitral valve disease.

MeSH

Intelligence, Mortality, Mitral Valve, Tomography, Artificial Intelligence, Transcatheter Mitral Valve Replacement