HeartFlow haemodynamic data may help predict coronary plaque rupture potential


First-in-human data presented at EuroPCR 2016 have demonstrated that haemodynamic data from HeartFlow may help predict which coronary plaques have the potential to rupture. The results were part of a study designed to determine whether use of HeartFlow technology could predict risk of developing acute coronary syndrome (ACS), a condition associated with sudden, reduced blood flow to the heart. The study was led by Bon-Kwon Koo of Seoul National University Hospital, Korea.

 The EMERALD (Exploring the mechanism of the plaque rupture in acute coronary syndrome using coronary computed tomography (CT) angiography and computational fluid dynamics) study evaluated 71 patients who experienced ACS and who had received a coronary CT angiography between one month and two years previously.

HeartFlow’s haemodynamic assessment, consisting of FFRCT, and more importantly the change in FFRCT across the plaque, was a better predictor of which plaques would rupture and lead to ACS than percent diameter stenosis or adverse plaque characteristics (APCs). APCs are features identifiable with coronary CT angiographies related to plaque composition and disease burden that increase the risk of rupture, as observed in prior clinical studies.

A total of 226 coronary plaques from the coronary CT angiographies were assessed—151 that had not ruptured (non-culprit lesions) and 75 that had ruptured and caused ACS (culprit lesions as identified by invasive angiography). In addition, combining HeartFlow’s haemodynamic assessment and APCs increased the discrimination of lesions at risk for causing ACS more than 10% over lesion severity alone.

“When evaluating a patient for risk of ACS, we need to look at not only the burden and composition of the plaque, but also the haemodynamic forces—or stress—placed upon the lesion,” Koo says. “The EMERALD study has demonstrated that by applying this important measurement we may have the potential to greatly improve prediction of the lesions that cause ACS, which could help us optimise treatment strategies for these high-risk patients.”

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