The Best Predictor of Ischemic Coronary Stenosis: Subtended Myocardial Volume, Machine Learning-based FFR, or High-risk Plaque Features?

Overview

Journal Eur Radiol

Specialty Radiology

Date 2019 Mar 24

PMID 30903334

Citations 14

Authors

Mengmeng Yu

Zhigang Lu

Chengxing Shen

Jing Yan

Yining Wang

Bin Lu

Jiayin Zhang

Affiliations

Soon will be listed here.

Abstract

Objectives: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR.

Methods: Patients who underwent both CCTA and FFR measurement within 2 weeks were retrospectively included. ML-based FFR, volume of subtended myocardium (V), percentage of subtended myocardium volume versus total myocardium volume (V), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR ≤ 0.8 were considered to be functionally significant.

Results: One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V, V, V/MLD, V/MLA, and LL/MLD were all significantly longer or larger in the group of FFR ≤ 0.8 while smaller minimal lumen area, MLD, and FFR value were noted. The AUC of FFR + V/MLD was significantly better than that of FFR alone (0.935 versus 0.873, p < 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V/MLD-complemented ML-based FFR for "gray zone" lesions with FFR value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208).

Conclusions: ML-based FFR simulation and V/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V/MLD is more accurate than ML-based FFR for lesions with simulated FFR value from 0.7 to 0.8.

Key Points: • Machine learning-based FFR and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis. • Subtended myocardium volume was more accurate than machine learning-based FFR for "gray zone" lesions with simulated FFR value from 0.7 to 0.8. • CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.

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