For patients with proximal atherosclerotic lesions, machine learning-based computed tomography (CT) angiography-derived fractional flow reserve (FFR) can diagnose functional ischemia with myocardial bridging and atherosclerotic disease, according to a study published in the Canadian Journal of Cardiology.

Researchers aimed to  assess the diagnostic ability of machine learning-based CT angiography-derived FFR to detect functional ischemia in myocardial bridging, and then compare those results with the results of the invasive FFR procedure in patients with left anterior descending myocardial bridging.

Patients underwent a coronary CT angiography, an invasive coronary angiography with FFR measurements, and a clinical exam. Myocardial bridging was measured from images and classified as superficial or deep and short or long. Coronary CT angiography data sets were used to measure FFR and then inputted into a machine-learning platform. Plaque calcification was evaluated using an arterial cross-section of the most severe lesion and then classified as none, mild, moderate, severe, or very severe.

Of the 104 patients with left anterior descending myocardial bridging included in this study, 72.1% were men, the mean age was 61.2 years old, 85.6% had superficial myocardial bridging, and 53.8% had short myocardial bridging. CT angiography-derived FFR showed functional ischemia in 52.9% of vessels, and invasive FFR showed functional ischemia in 46.2% of vessels.


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Using invasive FFR as the reference, CT angiography-derived FFR had a sensitivity of 0.96, a specificity of 0.84, an accuracy of 0.89, a positive predictive value of 0.84, and a negative predictive value of 0.96. No differences were found when analyzing superficial and deep myocardial bridging or short and long myocardial bridging (all P >.05). The accuracy of the CT angiography-derived FFR based on levels of stenosis, was 0.89 in vessels with a stenosis <50%, 0.82 in vessels with a stenosis of 50% to 69%, and 0.96 in vessels with a stenosis ≥70%.

Correlation comparison indicated a slight mean difference between invasive FFR and CT angiography-derived FFR of 0.014 in all myocardial bridge vessels, of 0.013 in vessels with a stenosis of 50% to 69%, of 0.014 in vessels with stenosis ≥70%, of 0.019 in the superficial, deep, and short myocardial bridge vessels, and of 0.007 in the long myocardial bridge vessels. Overall, the intraclass correlation coefficient was 0.775 (P <.001) between CT angiography-derived FFR and invasive FFR.

Limitations of this study include the relatively small sample size, the retrospective nature of the study, potential incomplete vasodilation in some patients not including interobserver agreement, calculating invasive FFR in the rest condition, and potential variables in the coronary CT angiography image quality.

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The researchers concluded “[machine learning-based CT angiography-derived FFR] has high diagnostic performance to identify functional ischemia in vessels with [myocardial bridge] and concomitant proximal atherosclerotic disease compared with invasive FFR,” but at this time, more studies are necessary before clinical use.

Disclosure: Several study authors declared affiliations with the pharmaceutical industry. Please see the original reference for a full list of authors’ disclosures.

Reference

Zhou F, Wang YN, Schoepf UJ, et al. Diagnostic performance of machine learning based CT-FFR in detecting ischemia in myocardial bridging and concomitant proximal atherosclerotic disease.Can J Cardiol. 2019;35(11):1523-1533.