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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.
Abstract:
Background: Coronary artery disease distribution along the vessel is a main determinant of FFR improvement after PCI. Identifying focal from diffuse disease from visual inspections of coronary angiogram (CA) and FFR pullback (FFR-PB) are operator-dependent. Computer science may standardize interpretations of such curves. Methods: A virtual stenting algorithm (VSA) was developed to perform an automated FFR-PB curve analysis. A survey analysis of the evaluations of 39 vessels with intermediate disease on CA and a distal FFR <0.8, rated by 5 interventional cardiologists, was performed. Vessel disease distribution and PCI strategy were successively rated based on CA and distal FFR (CA); CA and FFR-PB curve (CA/FFR-PB); and CA and VSA (CA/VSA). Inter-rater reliability was assessed using Fleiss kappa and an agreement analysis of CA/VSA rating with both algorithmic and human evaluation (operator) was performed. We hypothesize that VSA would increase rater agreement in interpretation of epicardial disease distribution and subsequent evaluation of PCI eligibility. Results: Inter-rater reliability in vessel disease assessment by CA, CA/FFR-PB, and CA/VSA were respectively, 0.32 (95% CI: 0.17-0.47), 0.38 (95% CI: 0.23-0.53), and 0.4 (95% CI: 0.25-0.55). The raters' overall agreement in vessel disease distribution and PCI eligibility was higher with the VSA than with the operator (respectively, 67 vs. 42%, and 80 vs. 70%, both p < 0.05). Compared to CA/FFR-PB, CA/VSA induced more reclassification toward a focal disease (92 vs. 56.2%, p < 0.01) with a trend toward more reclassification as eligible for PCI (70.6 vs. 33%, p = 0.06). Change in PCI strategy did not differ between CA/FFR-PB and CA/VSA (23.6 vs. 28.5%, p = 0.38). Conclusions: VSA is a new program to facilitate and standardize the FFR pullback curves analysis. When expert reviewers integrate VSA data, their assessments are less variable which might help to standardize PCI eligibility and strategy evaluations. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03824600.
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