Key result
Angiography-based quantitative flow ratio demonstrated excellent diagnostic performance for identifying hemodynamically significant cerebral artery stenosis, with an AUC of 0.946 for predicting FPR < 0.70.
Why the study?
With increasing attention to the hemodynamic evaluation of cerebral arterial stenosis, the study aimed to evaluate the performance of angiography-based quantitative flow ratio (QFR) against invasive fractional pressure ratios.
Does angiography-based quantitative flow ratio (QFR) accurately assess hemodynamic alterations caused by cerebral arterial stenosis compared to invasive fractional pressure ratios (FPRs) in patients with cerebral atherosclerosis?
Observational (n=29)
Single-blind
No
Does angiography-based quantitative flow ratio (QFR) accurately assess hemodynamic alterations caused by cerebral arterial stenosis compared to invasive fractional pressure ratios (FPRs) in patients with cerebral atherosclerosis?
Effect estimate: AUC 0.946 (95% CI 0.820-0.993)
Computational QFR from a single angiographic view provides high diagnostic accuracy for assessing the hemodynamic significance of cerebral arterial stenosis compared to invasive wire-based FPR.
QFR shows promise for cerebral stenosis assessment; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Increasing attention has been paid to the hemodynamic evaluation of cerebral arterial stenosis. We aimed to demonstrate the performance of angiography-based quantitative flow ratio (QFR) to assess hemodynamic alterations caused by luminal stenoses, using invasive fractional pressure ratios (FPRs) as a reference standard. METHODS: Between March 2013 and December 2019, 29 patients undergoing the pressure gradient measurement of cerebral atherosclerosis were retrospectively enrolled. Wire-based FPR was defined by the arterial pressure distal to the stenotic lesion (Pd) to proximal (Pa) pressure ratios (Pd/Pa). FPR < 0.70 or FPR < 0.75 was assumed as hemodynamically significant stenosis. The new method of computing QFR from a single angiographic view, i.e., the Murray law-based QFR, was applied to the interrogated vessel. An artificial intelligence algorithm was developed to realize the automatic delineation of vascular contour. RESULTS: < 0.001; mean difference (bias): -0.006, 95% limits of agreement: -0.198 to 0.209, respectively). Intra-observer and inter-observer reliability in QFR were excellent (intra-class correlation coefficients, 0.996 and 0.973, respectively). For predicting FPR < 0.70, the area under the receiver-operating characteristic curves (AUC) of QFR was 0.946 (95% CI, 0.820 to 0.993%). The sensitivity and specificity of QFR < 0.70 for identifying FPR < 0.70 was 88.9% (95% CI, 65.3 to 98.6%) and 85.0% (95% CI, 62.1 to 96.8%). For predicting FPR < 0.75, QFR showed similar performance with an AUC equal to 0.926. CONCLUSION: Computational QFR from a single angiographic view achieved comparable results to the wire-based FPR. The excellent diagnostic performance and repeatability empower QFR with high feasibility in the functional assessment of cerebral arterial stenosis.
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Huang et al. (2022) conducted an observational in Cerebral artery stenosis (n=29). Angiography-based quantitative flow ratio (QFR) vs. Invasive fractional pressure ratio (FPR) was evaluated on Diagnostic accuracy (AUC) for predicting FPR < 0.70 (AUC 0.946, 95% CI 0.820-0.993). Angiography-based quantitative flow ratio demonstrated excellent diagnostic performance for identifying hemodynamically significant cerebral artery stenosis, with an AUC of 0.946 for predicting FPR < 0.70.
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