Purpose: Wire-based coronary physiology pullback performed before percutaneous coronary intervention (PCI) discriminates coronary artery disease (CAD) distribution and extent, and is able to predict functional PCI result. No research investigated if quantitative flow ratio (QFR)–based physiology assessment is able to provide similar information. Methods: In 111 patients (120 vessels) treated with PCI, QFR was measured both before and after PCI. Pre-PCI QFR trace was used to discriminate functional patterns of CAD (focal, serial lesions, diffuse disease, combination). Functional CAD patterns were identified analyzing changes in the QFR virtual pullback trace (qualitative method) or after computation of the QFR virtual pullback index (QVPindex) (quantitative method). QVPindex calculation was based on the maximal QFR drop over 20 mm and the length of epicardial coronary segment with QFR most relevant drop. Then, the ability of the different functional patterns of CAD to predict post-PCI QFR value was tested. Results: By qualitative method, 51 (43%), 20 (17%), 15 (12%), and 34 (28%) vessels were classified as focal, serial focal lesions, diffuse disease, and combination, respectively. QVPindex values >0.71 and ≤0.51 predicted focal and diffuse patterns, respectively. Suboptimal PCI result (post-PCI QFR value ≤0.89) was present in 22 (18%) vessels. Its occurrence differed across functional patterns of CAD (focal 8% vs. serial lesions 15% vs. diffuse disease 33% vs. combination 29%, p=0.03). Similarly, QVPindex was correlated with post-PCI QFR value (r=0.62, 95% CI 0.50–0.72). Conclusion: Our results suggest that functional patterns of CAD based on pre-PCI QFR trace can predict the functional outcome after PCI. Clinical Trial Registration: ClinicalTrials.gov, number NCT02811796. Date of registration: June 23, 2016.

Angio-Based Fractional Flow Reserve, Functional Pattern of Coronary Artery Disease, and Prediction of Percutaneous Coronary Intervention Result: a Proof-of-Concept Study

Biscaglia S.
Primo
;
Tebaldi M.;Erriquez A.;Spitaleri G.;Scoccia A.;Zucchetti O.;D'Aniello E.;Manfrini M.;Pavasini R.;Campo G.
Ultimo
2022

Abstract

Purpose: Wire-based coronary physiology pullback performed before percutaneous coronary intervention (PCI) discriminates coronary artery disease (CAD) distribution and extent, and is able to predict functional PCI result. No research investigated if quantitative flow ratio (QFR)–based physiology assessment is able to provide similar information. Methods: In 111 patients (120 vessels) treated with PCI, QFR was measured both before and after PCI. Pre-PCI QFR trace was used to discriminate functional patterns of CAD (focal, serial lesions, diffuse disease, combination). Functional CAD patterns were identified analyzing changes in the QFR virtual pullback trace (qualitative method) or after computation of the QFR virtual pullback index (QVPindex) (quantitative method). QVPindex calculation was based on the maximal QFR drop over 20 mm and the length of epicardial coronary segment with QFR most relevant drop. Then, the ability of the different functional patterns of CAD to predict post-PCI QFR value was tested. Results: By qualitative method, 51 (43%), 20 (17%), 15 (12%), and 34 (28%) vessels were classified as focal, serial focal lesions, diffuse disease, and combination, respectively. QVPindex values >0.71 and ≤0.51 predicted focal and diffuse patterns, respectively. Suboptimal PCI result (post-PCI QFR value ≤0.89) was present in 22 (18%) vessels. Its occurrence differed across functional patterns of CAD (focal 8% vs. serial lesions 15% vs. diffuse disease 33% vs. combination 29%, p=0.03). Similarly, QVPindex was correlated with post-PCI QFR value (r=0.62, 95% CI 0.50–0.72). Conclusion: Our results suggest that functional patterns of CAD based on pre-PCI QFR trace can predict the functional outcome after PCI. Clinical Trial Registration: ClinicalTrials.gov, number NCT02811796. Date of registration: June 23, 2016.
2022
Biscaglia, S.; Uretsky, B. F.; Tebaldi, M.; Erriquez, A.; Brugaletta, S.; Cerrato, E.; Quadri, G.; Spitaleri, G.; Colaiori, I.; Di Girolamo, D.; Scoccia, A.; Zucchetti, O.; D'Aniello, E.; Manfrini, M.; Pavasini, R.; Barbato, E.; Campo, G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2460799
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