Xiaoling Zeng1, Emil Nielsen Holck2, Jelmer Westra2, Fukang Hu3, Jiayue Huang4, Hiroki Emori5, Takashi Kubo5, William Wijns4, Lianglong Chen1, Shengxian Tu1,3. 1. Department of Cardiology, Fujian Heart Medical Centre, Fujian Medical University Union Hospital, Fuzhou, China. 2. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark. 3. Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. 4. The Lambe Institute for Translational Medicine and Curam, National University of Ireland Galway, Galway, Ireland. 5. Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan.
Abstract
Background: Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels. Methods: This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention between August 2011 and October 2018 at Wakayama Medical University Hospital were included. All frames from OCT pullbacks were analyzed using a deep learning algorithm to obtain coronary plaque morphology including thin-cap fibroatheroma (TCFA), lipidic plaque volume (LPV), fibrous plaque volume (FPV), and calcific plaque volume (CPV). The interrogated vessels were stratified into three subgroups: the overestimation group with the numerical difference between the optical flow ratio (OFR) and FFR >0.05, the reference group with the difference ≥-0.05 and ≤0.05, and the underestimation group with the difference <-0.05. Results: In total 230 vessels with intermediate coronary artery stenosis from 193 patients were analyzed. The mean FFR was 0.82±0.10. Among them, 21, 179, and 30 vessels were in the overestimation, the reference, and the underestimation group, respectively. TCFA was higher in the underestimation group (60%) compared with reference (36.3%) and overestimation group (19%). Besides, it was not associated with numerical difference between OFR and FFR (NDOF) after multilevel linear regression. LPV was associated with NDOF as OFR underestimated FFR with -0.028 [95% confidence interval (CI): -0.047, -0.009] for every 100 mm3 increase in LPV. Conclusions: High lipid burden underestimates FFR when OFR is used to assess the hemodynamic importance of intermediate coronary artery stenosis. TCFA, FPV, and CPV were not independent predictors of NDOF. 2022 Cardiovascular Diagnosis and Therapy. All rights reserved.
Background: Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels. Methods: This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention between August 2011 and October 2018 at Wakayama Medical University Hospital were included. All frames from OCT pullbacks were analyzed using a deep learning algorithm to obtain coronary plaque morphology including thin-cap fibroatheroma (TCFA), lipidic plaque volume (LPV), fibrous plaque volume (FPV), and calcific plaque volume (CPV). The interrogated vessels were stratified into three subgroups: the overestimation group with the numerical difference between the optical flow ratio (OFR) and FFR >0.05, the reference group with the difference ≥-0.05 and ≤0.05, and the underestimation group with the difference <-0.05. Results: In total 230 vessels with intermediate coronary artery stenosis from 193 patients were analyzed. The mean FFR was 0.82±0.10. Among them, 21, 179, and 30 vessels were in the overestimation, the reference, and the underestimation group, respectively. TCFA was higher in the underestimation group (60%) compared with reference (36.3%) and overestimation group (19%). Besides, it was not associated with numerical difference between OFR and FFR (NDOF) after multilevel linear regression. LPV was associated with NDOF as OFR underestimated FFR with -0.028 [95% confidence interval (CI): -0.047, -0.009] for every 100 mm3 increase in LPV. Conclusions: High lipid burden underestimates FFR when OFR is used to assess the hemodynamic importance of intermediate coronary artery stenosis. TCFA, FPV, and CPV were not independent predictors of NDOF. 2022 Cardiovascular Diagnosis and Therapy. All rights reserved.
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