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Related Experiment Video

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Predicting paravalvular leak after transcatheter mitral valve replacement using commercially available software

Michael F Morris1, Alejandro Pena1, Aneesh Kalya1

  • 1Cardiovascular Institute, Banner University Medical Center, Phoenix, United States.

Journal of Cardiovascular Computed Tomography
|May 16, 2020
PubMed
Summary

Software modeling using computed tomography angiography (CTA) can predict moderate or severe mitral regurgitation (MR) risk after transcatheter mitral valve replacement (TMVR). This approach aids in identifying patients at risk for paravalvular leak (PVL) before the procedure.

Keywords:
Computed tomographyMitral regurgitationParavalvular leakSoftware modeling

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Area of Science:

  • Cardiovascular Imaging
  • Interventional Cardiology
  • Medical Software Development

Background:

  • Limited data exists on predicting significant mitral regurgitation (MR) post-transcatheter mitral valve replacement (TMVR).
  • Paravalvular leak (PVL) is a key cause of MR after TMVR.
  • Accurate risk stratification is crucial for patient selection and procedural success.

Purpose of the Study:

  • To evaluate the efficacy of software modeling based on computed tomography angiography (CTA) in predicting moderate or severe MR after TMVR.
  • To identify specific CTA-derived predictors of significant PVL.

Main Methods:

  • A cohort of 58 patients undergoing TMVR (valve-in-valve, valve-in-ring, valve-in-mitral annular calcification) was analyzed.
  • Pre-procedural CTA data was used with specialized software to model valve deployment and predict PVL.
  • Patient outcomes, including the development of moderate or severe MR due to PVL, were recorded.

Main Results:

  • The software model demonstrated high accuracy in predicting significant PVL (sensitivity 67%, specificity 96%, PPV 89%, NPV 86%).
  • Excellent inter-reader agreement was observed when using the software for PVL prediction (kappa 0.92).
  • On multivariate analysis, the presence of a gap between the virtual valve and mitral annulus on the software model was the strongest predictor of moderate/severe PVL (OR 36.8, p < 0.01).

Conclusions:

  • Pre-procedural CTA combined with software modeling offers a reliable and straightforward method for predicting MR risk due to PVL after TMVR.
  • This predictive tool can assist in optimizing patient selection and procedural planning for TMVR.