Publications by authors named "W Huey"
Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study.
J Soc Cardiovasc Angiogr Interv
March 2024
Background: Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making.
Methods: One hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers.
Article Synopsis
- - The study examines the relationship between luminal stenosis, computed tomography-derived fractional-flow reserve (FFR), and high-risk plaque features on coronary CT angiography, focusing on their impact on patient outcomes and plaque volume measurements.
- - Data from 4,430 patients were analyzed using artificial intelligence to assess coronary plaque and determine optimal plaque volume cutoffs, considering factors like age, sex, hypertension, and diabetes.
- - Results showed that patients with total plaque volume and percent atheroma volume above specific cutoffs faced higher risks of major adverse cardiac events and late revascularization within one year.
Article Synopsis
- - This study aimed to evaluate how coronary CT angiography (CCTA) and derived fractional flow reserve (FFR) are used clinically to assess coronary artery disease (CAD) in patients with diabetes mellitus (DM) compared to those without DM.
- - The analysis included 4,290 participants and found that patients with DM tended to have more severe CAD conditions, but both groups shared similar rates of treatment changes based on CT-FFR results and coronary revascularization.
- - Ultimately, while DM was linked to higher risk of adverse cardiovascular events over a year, it didn't significantly increase risk when accounting for the severity of arterial blockages.
Importance: Guidelines recommend deferral of testing for symptomatic people with suspected coronary artery disease (CAD) and low pretest probability. To our knowledge, no randomized trial has prospectively evaluated such a strategy.
Objective: To assess process of care and health outcomes in people identified as minimal risk for CAD when testing is deferred.