Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study

Jean-François Argacha1, Jean Decamp2, Bert Vandeloo1

  • 1Department of Cardiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium.

Insights

A new virtual stenting algorithm (VSA) standardizes the analysis of fractional flow reserve (FFR) pullback curves, improving inter-rater agreement on coronary artery disease distribution and percutaneous coronary intervention (PCI) eligibility.

Area of Science:

  • Cardiovascular Medicine
  • Medical Imaging Analysis
  • Computational Biology

Background:

  • Fractional flow reserve (FFR) improvement after percutaneous coronary intervention (PCI) depends on coronary artery disease distribution.
  • Visual interpretation of coronary angiogram (CA) and FFR pullback (FFR-PB) for disease identification is operator-dependent.
  • Computer science offers potential for standardizing FFR curve interpretations.

Observation:

  • A virtual stenting algorithm (VSA) was developed for automated FFR-PB curve analysis.
  • Interventional cardiologists (n=5) evaluated 39 vessels with intermediate disease using CA, CA/FFR-PB, and CA/VSA.
  • Inter-rater reliability for vessel disease assessment was evaluated using Fleiss kappa.

Findings:

  • Inter-rater reliability for vessel disease assessment was 0.32 (CA), 0.38 (CA/FFR-PB), and 0.4 (CA/VSA).
  • Overall agreement on disease distribution and PCI eligibility was higher with VSA (67%, 80%) than operator-based assessment (42%, 70%).
  • CA/VSA led to more reclassification toward focal disease (92% vs 56.2%) and a trend towards more PCI eligibility (70.6% vs 33%).

Implications:

  • VSA facilitates and standardizes FFR pullback curve analysis.
  • Integrating VSA data into expert assessments reduces variability in PCI eligibility and strategy evaluations.
  • This standardization may improve clinical decision-making for coronary artery disease management.

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