Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging

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 mm 3 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.

Dynamic natural morphologies and component changes in nonculprit subclinical atherosclerosis in patients with acute coronary syndrome at 1-year follow-up and clinical significance at 3-year follow-up

“…We first used novel OFR software that can precisely analyse various plaque components to study the dynamic natural morphologies and component changes in nonculprit subclinical atherosclerosis in patients with ACS based on OCT images from the subgroup data of our previous randomized controlled trial [30,31,40]. In our study, nonculprit lesions had regressed at 1-year follow-up in more than 50% of patients with ACS, even with standard LLT (statin ± ezetimibe).…”

“…Both ILUMIEN OPTIS and C7-XR (Lightlab Imaging Incorporated, Westford, MA) could be applied with a 2.7F (Dragonfly OPTIS or Dragonfly Duo imaging catheter, Westford, MA) catheter with automatic pullback at a speed of 36 mm/s with continuous contrast injection (3-4 ml/s) to remove blood cells. OCT images were analysed offline using a dedicated software (OctPlus, version V2, Pulse Medical, Shanghai, China) with artificial intelligence algorithms [30,31]. Quantitative planimetry measurements were obtained by automatic border detection followed by manual correction frame by frame.…”

“…The development of clinical coronary intravascular imaging (IVI) technology has led to breakthroughs in the analysis of the various components of coronary plaques [27][28][29][30][31]. Due to the limitations of various IVI technological features, most studies of plaque regression or progression are conducted with intravascular ultrasound (IVUS) alone or in combination with near-infrared spectroscopy (NIRS) [15][16][17][18][19][32][33][34][35][36][37].…”

“…Recently, a novel artificial intelligence framework, the optical flow ratio (OFR), was created for clinical use. This framework provides automatic plaque characterization and coronary physiological functional analysis software (Pulse Medical Imaging Technology, Shanghai, Co., Ltd) based on OCT data [30,31]. The purpose of this post hoc analysis was to analyse the changes in plaque components in nonculprit subclinical atherosclerosis and their correlation with changes in lipid profiles.…”

Angiographic quantitative flow ratio in acute coronary syndrome: beyond a tool to define ischemia-causing stenosis—a literature review

“…Indeed, the complex interactions exist between coronary physiology and morphological plaque characteristics, which may differ in hyperemic, non-hyperemic or computational physiological indices ( 88 ). More recently, Zeng et al ( 89 ) demonstrated that high lipid burden was associated with a numerical higher FFR value compared with OCT-derived computational FFR in assessing functional severity of coronary stenosis, which might be caused by larger luminal deformation of lipid-rich plaque.…”