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Peer-reviewedCC BYJournal of clinical medicine

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

  • Liliane Zillner1
  • Tillmann Kerbel1
  • Paul Sautner1
  • Mirjam G Wild2
  • Michaela M Hell3
  • Elmar W Kuhn4
  • Tanja K Rudolph5
  • Thomas Walther6
  • Lenard Conradi7
  • Andreas Zierer8
  • Francesco Maisano9
  • Marco Russo10
13 more authors
  • Fabrizio Rosati11
  • Andrea Colli12
  • Miguel Piñón13
  • David Reineke14
  • Gaby Aphram15
  • Christophe Dubois16,17
  • Jörg Hausleiter2,18
  • Ralph Stephan von Bardeleben3
  • Iuliana Coti1
  • Christian Loewe19
  • Daniel Zimpfer1
  • Georg Heinze20
  • Martin Andreas1,21
21 affiliations
  1. 1Department of Cardiac and Thoracic Aortic Surgery, Medical University of Vienna, 1090 Vienna, Austria.
  2. 2Medizinische Klinik I, LMU University Hospital, 81377 Munich, Germany.
  3. 3Department of Cardiology, University Medical Center Mainz, 55131 Mainz, Germany.
  4. 4Department of Cardiothoracic Surgery, Heart Center, University Hospital Cologne, 50937 Cologne, Germany.
  5. 5Department of Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, 44801 Bad Oeynhausen, Germany.
  6. 6Department of Cardiac Surgery, University Hospital Frankfurt, 60590 Frankfurt, Germany.
  7. 7Department of Cardiac Surgery, Heart Center, University Hospital Cologne, 50937 Cologne, Germany.
  8. 8Department for Cardiac, Vascular and Thoracic Surgery, Kepler University Hospital Linz, 4020 Linz, Austria.
  9. 9Valve Center, IRCCS Ospedale San Raffaele, University Vita-Salute San Raffaele, 20132 Milan, Italy.
  10. 10Department of Cardiac Surgery and Heart Transplantation, Azienda Ospedaliera San Camillo Forlanini, 00152 Rome, Italy.
  11. 11Division of Cardiac Surgery, Spedali Civili di Brescia, University of Brescia, 25121 Brescia, Italy.
  12. 12Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, 56126 Pisa, Italy.
  13. 13Servicio Cirugía Cardíaca, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain.
  14. 14Department of Cardiac Surgery, Inselspital University Hospital Bern, 3010 Bern, Switzerland.
  15. 15Department of Cardiovascular and Thoracic Surgery, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium.
  16. 16Department of Cardiovascular Medicine, University Hospital Leuven, 3000 Leuven, Belgium.
  17. 17Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium.
  18. 18German Centre for Cardiovascular Research, Munich Heart Alliance, 80336 Munich, Germany.
  19. 19Division of Cardiovascular and Interventional Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria.
  20. 20Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, 1090 Vienna, Austria.
  21. 21Division of Cardiac Surgery, Department of Surgery, Medical University Graz, 8010 Graz, Austria.
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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 TendyneTM 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.

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Cite

BibTeXRIS
BibTeX
@article{zillner2026artificial,
  title = {{Artificial Intelligence-Derived Computed Tomography Phenotyping for Cardiovascular Risk Stratification in Transcatheter Mitral Valve Replacement.}},
  author = {Liliane Zillner and Tillmann Kerbel and Paul Sautner and Mirjam G Wild and Michaela M Hell and Elmar W Kuhn and Tanja K Rudolph and Thomas Walther and Lenard Conradi and Andreas Zierer and Francesco Maisano and Marco Russo and Fabrizio Rosati and Andrea Colli and Miguel Piñón and David Reineke and Gaby Aphram and Christophe Dubois and Jörg Hausleiter and Ralph Stephan von Bardeleben and Iuliana Coti and Christian Loewe and Daniel Zimpfer and Georg Heinze and Martin Andreas},
  journal = {Journal of clinical medicine},
  year = {2026},
  month = sep,
  doi = {10.3390/jcm15197400},
  url = {https://europepmc.org/article/MED/42881862},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Artificial Intelligence-Derived Computed Tomography Phenotyping for Cardiovascular Risk Stratification in Transcatheter Mitral Valve Replacement.
AU  - Liliane Zillner
AU  - Tillmann Kerbel
AU  - Paul Sautner
AU  - Mirjam G Wild
AU  - Michaela M Hell
AU  - Elmar W Kuhn
AU  - Tanja K Rudolph
AU  - Thomas Walther
AU  - Lenard Conradi
AU  - Andreas Zierer
AU  - Francesco Maisano
AU  - Marco Russo
AU  - Fabrizio Rosati
AU  - Andrea Colli
AU  - Miguel Piñón
AU  - David Reineke
AU  - Gaby Aphram
AU  - Christophe Dubois
AU  - Jörg Hausleiter
AU  - Ralph Stephan von Bardeleben
AU  - Iuliana Coti
AU  - Christian Loewe
AU  - Daniel Zimpfer
AU  - Georg Heinze
AU  - Martin Andreas
PY  - 2026
DA  - 2026/09/23
JO  - Journal of clinical medicine
DO  - 10.3390/jcm15197400
UR  - https://europepmc.org/article/MED/42881862
AB  - 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 TendyneTM 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.
SN  - 2077-0383
ER  -