While FFR-CT is a reliable method for detecting lesion-specific ischemia, the derivation of FFR-CT is still a time-consuming process, according to presenter Dr. Christian Tesche from the Medical University of South Carolina in Charleston.
"Newer [FFR-CT] algorithms using machine learning may reduce the need for human input and improve the speed and clinical utility of [FFR-CT]," Tesche said.
The researchers sought to investigate the effect of coronary artery calcium (CAC) on the diagnostic performance of machine learning-based FFR-CT calculations for CCTA exams. They retrospectively assessed patients from the Machine Learning-Based CT Angiography Derived FFR: A Multi-Center Registry (MACHINE). These patients had undergone coronary CT angiography with calcium scoring followed by invasive FFR.
Using invasive FFR as the reference standard, the researchers found that Frontier -- a prototype deep-learning FFR-CT algorithm (Siemens Healthineers) -- demonstrated superior diagnostic performance over CCTA alone in detecting lesion-specific ischemia in calcified coronary arteries on both a per-lesion and per-patient level. The researchers also found no difference in the performance of FFR-CT between patients with low to intermediate Agatston scores (a measure of CAC) and those with high Agatston scores.
Evidence is rapidly accumulating in support of noninvasive FFR-CT calculation for guiding the management of patients with suspected coronary artery disease, Tesche noted.
"In this investigation, we demonstrated that [FFR-CT] has superior diagnostic performance over CCTA in a population of patients with a wide range of Agatston scores," he told AuntMinnie.com. "Further studies are needed to gauge the impact of machine learning-based CCTA-derived FFR determination. However, we believe that this approach may facilitate the integration of such algorithms into clinical decision-making trees for coronary artery disease management."
