Abstract Background Despite substantial advancements in coronary stent systems and implantation techniques, target vessel failure (TVF) remains a clinical issue to be addressed. Purpose We used deep learning algorithms for automatic stent analysis of intravascular ultrasound (IVUS) data to identify stent implantation characteristics associated with TVF. Methods In patients from the IVUS-guided group of the FLAVOUR trial who had undergone successful stenting, validated deep learning algorithms were applied to IVUS images to delineate plaque and stent strut for the quantification of stent expansion, apposition, and residual plaque burden. Stent overexpansion was defined as stent area exceeding 120% of the hypothetical healthy lumen area. Murray law-based quantitative flow ratio (μQFR) was also assessed from angiographic images. The primary endpoint was TVF at 2 years, defined as a composite of cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization. Results A total of 459 vessels in 441 patients were included, with TVF events occurred in 15 vessels (3.3%) during the 2-year follow-up. Stent analysis was performed in 687,379 stent struts from 70,369 IVUS image frames of the interrogated vessels with the assistance of specific deep learning algorithms. On vessel-level multivariate analysis adjusting for procedural covariates, stent overexpansion length >5 mm (hazard ratio, 4.57; 95% confidence interval [CI], 1.31 to 15.92; P=0.017), residual plaque burden at proximal stent edge >50% (hazard ratio, 3.34; 95% CI, 1.19 to 9.33; P=0.022), and post-PCI μQFR <0.90 (hazard ratio, 5.09; 95% CI, 1.62 to 15.97; P=0.005) were more likely to be present in vessels with TVF events than in those without. Conclusions Besides the established indexes of post-procedural residual plaque burden and low μQFR, deep learning-powered IVUS stent overexpansion was significantly associated with increased risk of TVF.Suboptimal stenting results and TVF
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