Aortic valve calcium score as a predictor for outcome after TAVI using the CoreValve revalving system

Alexander W Leber1, Markus Kasel, Thomas Ischinger

  • 1Department of Cardiology, Heart Center Bogenhausen, Klinikum München Bogenhausen, Technical University of Munich, Germany. leber@heart-center-muc.de

Insights

The aortic valve calcification score, determined via CT scans, accurately predicts outcomes after transcatheter aortic valve implantation (TAVI). This score helps assess risks, complications, and functional improvement post-TAVI.

Area of Science:

  • Cardiology
  • Radiology
  • Medical Imaging

Background:

  • Transcatheter aortic valve implantation (TAVI) is a key treatment for high-risk surgical patients.
  • Existing surgical risk scores inadequately predict TAVI outcomes.
  • There is a need for alternative risk stratification parameters in TAVI.

Purpose of the Study:

  • To assess the predictive value of CT-derived aortic valve calcification score for TAVI outcomes.
  • To determine if aortic valve calcification can identify patients at higher risk for complications and mortality.
  • To explore the association between calcification score and functional improvement after TAVI.

Main Methods:

  • 68 patients undergoing transfemoral TAVI with the CoreValve device were analyzed.
  • Aortic valve calcium scoring was performed using preprocedural 64-slice ECG-gated CT scans.
  • Statistical analysis included linear regression to identify predictors of outcomes.

Main Results:

  • The aortic valve calcium score significantly predicted 30-day Major Adverse Cardiovascular Events (MACE) and 1-year mortality.
  • Higher calcium scores (>750) correlated with significantly lower 1-year survival rates (58% vs. 98%).
  • The score was associated with post-procedural aortic regurgitation and inversely with NYHA-class improvement.

Conclusions:

  • Aortic valve calcification degree predicts TAVI complications, mortality, and functional recovery.
  • CT-derived aortic valve calcium scoring is a valuable tool for risk stratification and patient selection in TAVI.
  • This parameter can be integrated into pre-procedural planning using existing CT data.
Abstract

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