J Cardiovasc Comput Tomogr. 2024 Apr 26:S1934-5925(24)00100-X. doi: 10.1016/j.jcct.2024.04.009. Online ahead of print.

ABSTRACT

BACKGROUND: Positive remodeling is an integral part of the vascular adaptation process during the development of atherosclerosis, which can be detected by coronary computed tomography angiography (CTA).

METHODS: A total of 426 patients who underwent both coronary CTA and optical coherence tomography (OCT) were included. Four machine learning (ML) models, gradient boosting machine (GBM), random forest (RF), deep learning (DL), and support vector machine (SVM), were employed to detect specific plaque features. A total of 15 plaque features assessed by OCT were analyzed. The variable importance ranking was used to identify the features most closely associated with positive remodeling.

RESULTS: In the variable importance ranking, lipid index and maximal calcification arc were consistently ranked high across all four ML models. Lipid index and maximal calcification arc were correlated with positive remodeling, showing pronounced influence at the lower range and diminishing influence at the higher range. Patients with more plaques with positive remodeling throughout their entire coronary trees had higher low-density lipoprotein cholesterol levels and were associated with a higher incidence of cardiovascular events during 5-year follow-up (Hazard ratio 2.10 [1.26-3.48], P ​= ​0.004).

CONCLUSION: Greater lipid accumulation and less calcium burden were important features associated with positive remodeling in the coronary arteries. The number of coronary plaques with positive remodeling was associated with a higher incidence of cardiovascular events.

PMID:38677958 | DOI:10.1016/j.jcct.2024.04.009