Machine Learning–Driven Risk Prediction Model in Transthyretin Amyloid Cardiomyopathy - Takeaways - MDSpire
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A Machine Learning Model for Predicting Risk in Transthyretin Amyloid Cardiomyopathy

  • By

  • Giovanni Baj

  • Nicola Ciocca

  • Pooya Mohammadi Kazaj

  • Xuan Ma

  • Annina A. Studer Bruengger

  • Simon F. Stämpfli

  • Niklas F. Ehl

  • Sarah Hugelshofer

  • Otmar Pfister

  • Joëlle Lehmann

  • Christoph Ryffel

  • Lukas Hunziker

  • Michael Poledniczek

  • Andreas Kammerlander

  • George C. M. Siontis

  • Stephan Windecker

  • Moritz J. Hundertmark

  • Christian Nitsche

  • Isaac Shiri

  • Christoph Gräni

  • October 1, 2026

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  • 1

    The study developed and validated an artificial intelligence model to predict all-cause mortality or heart failure hospitalization in patients with transthyretin cardiac amyloidosis.

  • 2

    Patients with confirmed transthyretin cardiac amyloidosis were prospectively recruited from 6 Swiss centers and the Medical University of Vienna.

  • 3

    The primary outcome was all-cause mortality or hospitalization for heart failure, documented during structured follow-up beginning 6 months after diagnosis.

  • 4

    A random survival forest used clinical, laboratory, imaging, and genetic data, with missing-value imputation and LASSO feature selection.

  • 5

    Internal-external cross-validation successively reserved each center as an untouched test set while the model was developed using data from the remaining centers.

Original Source(s)

Machine Learning–Driven Risk Prediction Model in Transthyretin Amyloid Cardiomyopathy

  • by Giovanni Baj, Nicola Ciocca, Pooya Mohammadi Kazaj, Xuan Ma, Annina A. Studer Bruengger, Simon F. Stämpfli, Niklas F. Ehl, Sarah Hugelshofer, Otmar Pfister, Joëlle Lehmann, Christoph Ryffel, Lukas Hunziker, Michael Poledniczek, Andreas Kammerlander, George C. M. Siontis, Stephan Windecker, Moritz J. Hundertmark, Christian Nitsche, Isaac Shiri, Christoph Gräni

  • October 1, 2026

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