SmartFFR, a New Functional Index of Coronary Stenosis: Comparison With Invasive FFR Data

Author:

Siogkas Panagiotis K.,Lakkas Lampros,Sakellarios Antonis I.,Rigas George,Kyriakidis Savvas,Stefanou Kostas A.,Anagnostopoulos Constantinos D.,Clemente Alberto,Rocchiccioli Silvia,Pelosi Gualtiero,Parodi Oberdan,Papafaklis Michail I.,Naka Katerina K.,Michalis Lampros K.,Neglia Danilo,Fotiadis Dimitrios I.

Abstract

Aims: In this study, we evaluate the efficacy of SmartFFR, a new functional index of coronary stenosis severity compared with gold standard invasive measurement of fractional flow reserve (FFR). We also assess the influence of the type of simulation employed on smartFFR (i.e. Fluid Structure Interaction vs. rigid wall assumption).Methods and Results: In a dataset of 167 patients undergoing either computed tomography coronary angiography (CTCA) and invasive coronary angiography or only invasive coronary angiography (ICA), as well as invasive FFR measurement, SmartFFR was computed after the 3D reconstruction of the vessels of interest and the subsequent blood flow simulations. 202 vessels were analyzed with a mean total computational time of seven minutes. SmartFFR was used to process all models reconstructed by either method. The mean FFR value of the examined dataset was 0.846 ± 0.089 with 95% CI for the mean of 0.833–0.858, whereas the mean SmartFFR value was 0.853 ± 0.095 with 95% CI for the mean of 0.84–0.866. SmartFFR was significantly correlated with invasive FFR values (RCCTA = 0.86, pCCTA < 0.0001, RICA = 0.84, pICA < 0.0001, Roverall = 0.833, poverall < 0.0001), showing good agreement as depicted by the Bland-Altman method of analysis. The optimal SmartFFR threshold to diagnose ischemia was ≤0.83 for the overall dataset, ≤0.83 for the CTCA-derived dataset and ≤0.81 for the ICA-derived dataset, as defined by a ROC analysis (AUCoverall = 0.956, p < 0.001, AUCICA = 0.975, p < 0.001, AUCCCTA = 0.952, p < 0.001).Conclusion: SmartFFR is a fast and accurate on-site index of hemodynamic significance of coronary stenosis both at single coronary segment and at two or more branches level simultaneously, which can be applied to all CTCA or ICA sequences of acceptable quality.

Funder

Horizon 2020

Publisher

Frontiers Media SA

Subject

Cardiology and Cardiovascular Medicine

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2. Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

3. Graph-guided Gaussian Process-based Diagnosis of CVD Severity with Uncertainty Measures;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

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