Diagnostics (Oct 2024)

Novel Insights into Non-Invasive Diagnostic Techniques for Cardiac Amyloidosis: A Critical Review

  • Marco Maria Dicorato,
  • Paolo Basile,
  • Giuseppe Muscogiuri,
  • Maria Cristina Carella,
  • Maria Ludovica Naccarati,
  • Ilaria Dentamaro,
  • Marco Guglielmo,
  • Andrea Baggiano,
  • Saima Mushtaq,
  • Laura Fusini,
  • Gianluca Pontone,
  • Cinzia Forleo,
  • Marco Matteo Ciccone,
  • Andrea Igoren Guaricci

DOI
https://doi.org/10.3390/diagnostics14192249
Journal volume & issue
Vol. 14, no. 19
p. 2249

Abstract

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Cardiac amyloidosis (CA) is a cardiac storage disease caused by the progressive extracellular deposition of misfolded proteins in the myocardium. Despite the increasing interest in this pathology, it remains an underdiagnosed condition. Non-invasive diagnostic techniques play a central role in the suspicion and detection of CA, also thanks to the continuous scientific and technological advances in these tools. The 12-lead electrocardiography is an inexpensive and reproducible test with a diagnostic accuracy that, in some cases, exceeds that of imaging techniques, as recent studies have shown. Echocardiography is the first-line imaging modality, although none of its parameters are pathognomonic. According to the 2023 ESC Guidelines, a left ventricular wall thickness ≥ 12 mm is mandatory for the suspicion of CA, making this technique crucial. Cardiac magnetic resonance provides high-resolution images associated with tissue characterization. The use of contrast and non-contrast sequences enhances the diagnostic power of this imaging modality. Nuclear imaging techniques, including bone scintigraphy and positron emission tomography, allow the detection of amyloid deposition in the heart, and their role is also central in assessing the prognosis and response to therapy. The role of computed tomography was recently evaluated by several studies, above in population affected by aortic stenosis undergoing transcatheter aortic valve replacement, with promising results. Finally, machine learning and artificial intelligence-derived algorithms are gaining ground in this scenario and provide the basis for future research. Understanding the new insights into non-invasive diagnostic techniques is critical to better diagnose and manage patients with CA and improve their survival.

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