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

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.

Keywords: computer science; coronary artery disease; coronary physiology; fractional flow reserve; percutaneous coronary intervention; pullback; vessel disease distribution; virtual coronary stenting.

Conflict of interest statement

QH: received research grant and speaker honoraria from Abbott Vascular. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Argacha, Decamp, Vandeloo, Babin, Lochy, Van den Bussche, de Hemptinne, Xaplanteris, Magne, Segers and Cosyns.

Figures

Figure 1

Study design.

Figure 2

Illustrative report of VSA analysis.

Figure 3

Overall agreement of ratings performed in CA/VSA setting with operator and VSA evaluation of vessel disease category and PCI eligibility.

Figure 4

Reclassification of vessel disease category and PCI eligibility by visual interpretation (CA/FFR-PB) and VSA facilitated interpretation (CA/VSA) of FFR-PB curves. Detailed reclassification diagram of vessel disease category (A) and PCI eligibility (B). Global trend of reclassification in focal disease category (C) and eligible PCI (D).

Figure 5

Illustrative case of change in PCI strategy by VSA analysis. (A) Baseline CA; (B) Stent positioning according to CA; (C) Raw FFR-PB curve; (D) VSA analysis reporting lambda coefficient and stent position; (E) Virtual stenting according to VSA; (F) Predicted effect of stenting on post-PCI FFR (red curves) compared to baseline FFR curves (blue curve). Based on CA (A), most of raters proposed a PCI of the mid LAD-D1 bifurcation (B). Facilitated interpretation of FFR-PB curves (C) by VSA (D) recommend a more proximal stenting from mm 7–26 by using a 19 mm sent over the distal LM-LAD bifurcation. FFR-PB curves (E). Prediction of the effect of the stent on post-PCI FFR are presented as a curve (F).