Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery

Abstract

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using deep learning models, we extracted geometrical parameters and maximal attenuation (or calcium) burden index (ABI-max or CBI-max) from the intravascular ultrasound (IVUS) images of nonculprit vessels in 1115 patients. The endpoints included cardiac death, myocardial infarction, and target vessel revascularization of nonculprit vessel. Cardiac death occurred in 27 (2.4 %) patients at 3 years and 39 (3.5 %) patients at 5 years. At 5 years, the cardiac death-free survival rate was significantly lower with ABP-max ≥11.37 % vs. < 11.37 % (90.0 % vs. 98.7 %), CBI-max ≥13.40 % vs. < 13.40 % (92.8 % vs. 98.4 %), and percent atheroma volume ≥ 51.35 % vs. < 51.35 % (94.0 % vs. 97.7) (all log-rank p < 0.001). The independent predictors of 5-year cardiovascular mortality were age (hazard ratio [HR] 1.21), female sex (HR 0.33), history of heart failure (HR 6.06), chronic kidney disease (HR 18.28), ABI-max (HR 1.04), and CBI-max (HR 1.05). The independent determinants of 5-year target vessel revascularization of nonculprit vessel were fractional flow reserve (HR 0.95 per 0.01 increase), minimal lumen area (HR 0.63), and plaque burden (HR 1.15). In patients with nonculprit coronary artery lesions, a large burden of attenuated or calcified plaques predicted cardiac mortality, while IVUS geometry was associated with repeat revascularization. Thus, deep learning-based IVUS analysis of the whole target vessel may help clinicians identify high-risk lesions.