Results from the FLASH trial reveal that AI-based fully automated quantitative coronary angiography (AI-QCA)-assisted PCI is noninferior to optical coherence tomography (OCT)-guided PCI in achieving an optimal minimal stent area. Both methods showed comparable procedural complications, OCT-defined endpoints, and six-month clinical outcomes, as presented at TCT 2024.
The trial, which was also published in JACC: Cardiovascular Interventions, included 400 patients with significant coronary artery disease undergoing PCI at 13 centers in South Korea. Participants were randomized to either AI-QCA-assisted or OCT-guided PCI. The primary endpoint, the post-PCI minimal stent area (MSA) assessed by OCT, measured 6.3 ± 2.2 mm² in the AI-QCA group and 6.2 ± 2.2 mm² in the OCT group (difference, -0.16; 95% CI, -0.59 to 0.28; P for noninferiority <0.001). Other OCT-defined endpoints such as stent under-expansion, dissection, and untreated reference segment disease showed no significant differences between the groups. However, there was a higher incidence of stent malapposition in the AI-QCA group (13.6% vs. 5.6%).
The researchers note that intracoronary imaging-guided PCI has been shown to improve clinical outcomes over angiography-guided PCI, especially in complex coronary artery disease. However, its global utilization remains low due to various clinical, logistic, and economic constraints. The FLASH trial highlights AI-QCA as a promising alternative for guiding coronary interventions, especially in resource-limited settings or less complex coronary artery disease cases, where the full benefits of intravascular imaging are not yet established.
Looking ahead, researchers suggest larger clinical trials focusing on long-term clinical outcomes to further define the role of AI-QCA-assisted PCI in interventional cardiology.
In a related editorial, experts underscore the FLASH trial as a significant step toward addressing unmet needs in interventional cardiology through advanced technology. They emphasize that while much work remains, the field is moving closer to a future where AI solutions are central to diagnosing and treating CAD in the catheterization laboratory.
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