Dataset Information

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

ABSTRACT:

Background

This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFR_CT), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease.

Methods

The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFR_CT, and FFR in 164 vessels of 144 patients. Obstructive stenosis was defined as stenosis ≥50%. An area under the receiver -operating characteristics curve (AUC) analysis was conducted to define the optimal thresholds for ΔFFR_CT and the plaque variables. Ischemia was defined as a FFR of ≤0.80.

Results

The optimal cut-off value of ΔFFR_CT was 0.14. Low-attenuation plaque (LAP) ≥76.23 mm³ and a percentage aggregate plaque volume (%APV) ≥28.91% can be used to predict ischemia independent of other plaque characteristics. The addition of LAP ≥76.23 mm³ and %APV ≥28.91% improved the discrimination (AUC, 0.742 vs. 0.649, P=0.001) and reclassification abilities [category-free net reclassification index (NRI), 0.339, P=0.027; relative integrated discrimination improvement (IDI) index, 0.093, P<0.001] of the assessments compared to the stenosis evaluation alone, and the addition of information about ΔFFR_CT ≥0.14 further increased the discrimination (AUC, 0.828 vs. 0.742, P=0.004) and reclassification abilities (NRI, 1.029, P<0.001; relative IDI, 0.140, P<0.001) of the assessments.

Conclusions

The addition of the plaque assessment and ΔFFR_CT to the stenosis assessments improved the identification of ischemia compared to the stenosis assessment alone.

SUBMITTER: Yan H

PROVIDER: S-EPMC10239986 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

Yan Hankun H Zhao Na N Geng Wenlei W Yu Xianbo X Gao Yang Y Lu Bin B

Quantitative imaging in medicine and surgery 20230420 6

<h4>Background</h4>This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFR<sub>CT</sub>), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease.<h4>Methods</h4>The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFR<sub>CT</sub>, and FFR in 164 vessels of 144 patients. ...[more]

PMID: 37284071

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Project description:BackgroundA pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET).MethodsThis post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT).ResultsOne hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16).ConclusionsAn externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.

Project description:Amongst patients with suspected obstructive coronary artery disease (CAD), less than a third of patients have obstructive disease on invasive coronary angiography (ICA) and fewer still have flow-limiting obstructive disease as determined by invasive fractional flow reserve (FFR). FFR is a powerful tool in guiding revascularization of flow-limiting lesions which in turn improves clinical outcome in those with haemodynamically significant obstructive disease. However FFR is infrequently performed due to the cost, time and patient discomfort the procedure entails. Further advances in non-invasive imaging has allowed FFR to be derived non-invasively by applying computational fluid dynamic (CFD) modeling to the coronary computed tomography angiography (CCTA) dataset without the need to induce hyperemia or modify the standard CCTA acquisition protocol. FFR derived from CCTA has been shown to have excellent correlation with invasive FFR and remains diagnostically robust in presence of reduced signal-to-noise ratio (SNR), coronary calcification and motion artifact. More recently, new data have emerged evaluating the clinical impact of fractional flow reserve computed tomography (FFRCT) on the assessment and management of patients with stable chest pain. One such study is the Prospective LongitudinAl trial of FFRCT: Outcome and Resource IMpacts (PLATFORM) study which showed an improved patient selection for ICA using CCTA-FFRCT approach by increasing the likelihood of identifying obstructive CAD at ICA amongst those intended for invasive testing. CCTA-FFRCT may therefore serve as efficacious gatekeeper to ICA that enriches the ICA population. The utility of FFRCT has also helped deepened our understanding of CAD. Through CFD modeling, it is now recognized that there are mechanistic forces of wall shear stress (WSS) and axial plaque force acting on coronary plaques. This has created further interest in exploring the possible interplay between these mechanistic forces on the development of coronary plaque and vulnerability of these plaques to rupture.

Dataset Information

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

Background

Methods

Results

Conclusions

Publications

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion.

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