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

Morphologies and composition changes in nonculprit subclinical atherosclerosis in diabetic versus nondiabetic patients with acute coronary syndrome who underwent long-term statin therapy

“…The main cause of recurrent MACEs in ACS patients is not only culprit lesions; approximately 50% of the causes are from the progression of nonculprit lesions 43 , and more aggressive LLT (including statins, ezetimibe, and PCSK9i) is conducive to the regression of nonculprit lesions 58 , 59 . Patients with PR have a significantly lower 1–3-year incidence of cardiac events 18 , 60 – 62 . Therefore, the outcome of nCSA in ACS patients after PCI for culprit lesions is a key issue that our clinicians should focus on.…”

“…The development in data analytics and digitized medical imaging has enabled the clinical application of AI to improve the acute judgement of the characteristics of coronary lesions and optimization of the PCI procedure, which can improve patient outcomes 27 , 28 , 63 . OFR is an AI software developed based on OCT data analysis in recent years that can automatically identify the morphology and composition of coronary plaque 18 , 27 , 28 . In this observational study, we used this software to retrospectively analyse the OCT data of our previous RCT studies based on the concept of AI.…”

“…The segment of interest for OFR measurement was defined as at least 15 mm long of maximal plaque-containing region (at least plaque burden of ≥ 30%). The measurement index is summarized as follows as in previous studies 18 , 33 : total atheroma volume (TAV), percent atheroma volume (PAV), fibrous TAV or PAV, calcium TAV or PAV, macrophage TAV or PAV, and thinnest fibrous cap thickness (TFCT) in the analysed segment. The normalized TAV (TAVn) was calculated as TAVn = TAV/number of frames of measurement segment × 100 to balance the difference between the absolute value of the lesion in different patients.…”

“…More studies are based on semiquantitative analysis of optical coherence tomography (OCT) data, especially for lipids, calcified plaque and fibrous cap thickness, but the quantification of plaque components based on OCT is insufficient 9 , 25 , 26 . Recently, an artificial intelligence (AI) framework named the optical flow ratio (OFR) based on OCT data was used to automatically analyse the components and volume of coronary plaque precisely for clinical use (Pulse Medical Imaging Technology, Shanghai, Co., Ltd.) 18 , 27 , 28 . The purpose of this post hoc analysis was to compare changes in plaque morphology and composition in nonculprit subclinical atherosclerosis (nCSA) by analysing the OFR in DM patients versus patients without DM (non-DM) at the 1-year follow-up.…”

Coronary artery properties in atherosclerosis: A deep learning predictive model

“…Based on the information taken from the IVUS images previously published ( Le Floc’h et al., 2009 ) and the anatomy of the atherosclerotic coronary artery, we performed the Finite Element Method (FEM) on 540 different 2D idealized geometries. As seen in other works ( Nong et al., 2022 ; Shen et al., 2022 ; Le Floc’h et al., 2009 ; Finet et al., 2004 ), 2D models are commonly used when artery geometries are obtained from the segmentation of IVUS images. One of the key advantages of using 2D models for studying the mechanical properties of atherosclerotic coronary arteries is that the IVUS images provide 2D cross-sectional information of the coronary artery geometry.…”