A multi-parametric predictive model incorporating clinical, lipidomics, 3D imaging, and computational data demonstrated 88% accuracy in predicting site-specific coronary plaque progression.
Cohort (n=187)
Does a multi-parametric predictive model using lipidomics, blood flow, and LDL transport modeling improve the prediction of site-specific coronary atherosclerotic plaque progression in patients with stable CAD?
A multi-parametric model incorporating lipidomics, blood flow, and LDL transport modeling accurately predicts site-specific coronary plaque progression on CCTA.
Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 ± 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.
Sakellarios et al. (Wed,) conducted a cohort in stable coronary artery disease (CAD) (n=187). Multi-parametric predictive model vs. Current computational models was evaluated on Site-specific plaque progression. A multi-parametric predictive model incorporating clinical, lipidomics, 3D imaging, and computational data demonstrated 88% accuracy in predicting site-specific coronary plaque progression.