October 26, 2011Journal of the American College of Cardiology
Diagnosis of Ischemia-Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiograms
Key result
CCTA-derived FFR outperforms CCTA stenosis for detecting ischemia-causing lesions, achieving ~84% accuracy.
- AUC 0.90
- P=0.001
- n=103
Why the study?
Invasive FFR identifies ischemia-causing lesions, but the diagnostic performance of noninvasively computing FFR from CCTA data was unknown.
Does noninvasive FFR computed from CCTA accurately identify ischemia-causing coronary stenoses compared to invasive FFR in patients with suspected or known CAD?
Key Points
- This study aims to evaluate the diagnostic performance of a noninvasive method for quantifying fractional flow reserve from coronary computed tomography angiography.
- Computation of fractional flow reserve from CCTA performed on 159 vessels in 103 patients.
- Ischemia defined by FFR(CT) and FFR ≤0.80; obstructive CAD by CCTA stenosis ≥50%.
- Diagnostic performance assessed using invasive FFR as the reference standard.
- 56% of patients had ≥1 vessel with FFR ≤0.80.
- FFR(CT) accuracy was 84.3% with a sensitivity of 87.9% and specificity of 82.2%; CCTA stenosis accuracy was 58.5%.
- Area under the curve for FFR(CT) was 0.90, significantly higher than CCTA at 0.75 (p = 0.001).
Study Design
Type
Cross-Sectional (n=103)
Structured PICO
Does noninvasive FFR computed from CCTA accurately identify ischemia-causing coronary stenoses compared to invasive FFR in patients with suspected or known CAD?
P
Population
103 patients (159 vessels) with suspected or known coronary artery disease (CAD) undergoing CCTA, invasive coronary angiography, and FFR.
I
Intervention
Computation of fractional flow reserve from CCTA data (FFR(CT)) using computational fluid dynamics
C
Comparator
Invasive FFR (reference standard) and CCTA stenosis severity (≥50%)
O
Outcome
Diagnostic performance (accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC) for identifying ischemia-causing stenosis (FFR ≤0.80)surrogate
Main Result
Effect estimate: AUC 0.90
Absolute Event Rate: 84.3% vs 58.5%
p-value: p=0.001
Key expert perspectives
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
SAStephan AchenbachCardiologist, University of Giessen
“It is an impressive first step into what needs to follow, the painstakingly detailed workup of whether this method will translate into clinical benefit when applied to a broader scale and which patient groups are the ones to most likely benefit from this additional analysis.”
“The research demonstrates that there is unlimited potential for software to tell us more about the physiology of the anatomy and to be more accurate in detecting patients with significant coronary artery disease. However, we have to put things in perspective, and it is not ready to be applied to day-to-day practice.”
“Based upon several prospective multicenter studies, FFRCT has been proven a disruptive technology that harnesses the power of computational fluid dynamics for diagnosis of physiologically-relevant coronary heart disease. Coupled with the anatomic information provided by coronary CT angiography, FFRCT for the first time facilitates a 'one stop shop' for discrimination of individuals who suffer from myocardial ischemia as well as the identification of the specific coronary artery lesions that are the cause.”
Implication
Noninvasive FFR derived from CCTA provides high diagnostic performance for detecting and excluding ischemia-causing coronary lesions, outperforming standard CCTA stenosis assessment.
Abstract
OBJECTIVES: The aim of this study was to determine the diagnostic performance of a new method for quantifying fractional flow reserve (FFR) with computational fluid dynamics (CFD) applied to coronary computed tomography angiography (CCTA) data in patients with suspected or known coronary artery disease (CAD). BACKGROUND: Measurement of FFR during invasive coronary angiography is the gold standard for identifying coronary artery lesions that cause ischemia and improves clinical decision-making for revascularization. Computation of FFR from CCTA data (FFR(CT)) provides a noninvasive method for identifying ischemia-causing stenosis; however, the diagnostic performance of this new method is unknown. METHODS: Computation of FFR from CCTA data was performed on 159 vessels in 103 patients undergoing CCTA, invasive coronary angiography, and FFR. Independent core laboratories determined FFR(CT) and CAD stenosis severity by CCTA. Ischemia was defined by an FFR(CT) and FFR ≤0.80, and anatomically obstructive CAD was defined as a CCTA with stenosis ≥50%. Diagnostic performance of FFR(CT) and CCTA stenosis was assessed with invasive FFR as the reference standard. RESULTS: Fifty-six percent of patients had ≥1 vessel with FFR ≤0.80. On a per-vessel basis, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 84.3%, 87.9%, 82.2%, 73.9%, 92.2%, respectively, for FFR(CT) and were 58.5%, 91.4%, 39.6%, 46.5%, 88.9%, respectively, for CCTA stenosis. The area under the receiver-operator characteristics curve was 0.90 for FFR(CT) and 0.75 for CCTA (p = 0.001). The FFR(CT) and FFR were well correlated (r = 0.717, p < 0.001) with a slight underestimation by FFR(CT) (0.022 ± 0.116, p = 0.016). CONCLUSIONS: Noninvasive FFR derived from CCTA is a novel method with high diagnostic performance for the detection and exclusion of coronary lesions that cause ischemia.
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Cite This Study
Koo et al. (2011) conducted a cross-sectional in Suspected or known coronary artery disease (n=103). FFR(CT) vs. CCTA stenosis was evaluated on Diagnostic accuracy for identifying ischemia-causing stenosis (invasive FFR ≤0.80) (AUC 0.90, p=0.001). Noninvasive FFR derived from CCTA showed higher diagnostic accuracy (84.3% vs 58.5%) and AUC (0.90 vs 0.75, p=0.001) than CCTA stenosis for detecting ischemia-causing coronary lesions.