Impact of Intravascular Ultrasound-Based Artificial Intelligence on Outcomes After Percutaneous Coronary Intervention (INNOVATE-PCI Study)

What this Trial Is About

Researchers are evaluating the use of machine learning-based models derived from coronary angiography and intravascular ultrasound (IVUS) to improve decision making and stent optimization in patients with coronary artery disease. This multicenter, prospective study plans to enroll 3,000 patients from 15 centers in South Korea between January 2020 and June 2025. The study focuses on both treated coronary lesions requiring stent implantation and deferred lesions with more than 30% stenosis to assess the models' diagnostic performance and clinical impact. Participants will undergo percutaneous coronary intervention (PCI) guided by IVUS to implant stents. The study uses supervised machine learning algorithms to predict fractional flow reserve (FFR), characterize plaques, predict stent expansion, and forecast stent failure after implantation. Both culprit (treated) and nonculprit (deferred) coronary lesions will be monitored, with the primary goal of evaluating target vessel failure (TVF) related to these lesions over two years. During the study, participants will be closely followed for two years after their stent implantation. Researchers will assess clinical outcomes related to treated and deferred lesions, focusing on the occurrence of target vessel failure. The study involves detailed imaging and algorithm evaluation to determine the prognostic impact of the new AI technologies on event reduction following PCI.

Clinical Impact of Intravascular Ultrasound-Based Artificial Intelligence Technologies (INNOVATE-PCI)