Key result
The simplified FFRsim model derived from 3D coronary angiography demonstrated high diagnostic accuracy (AUC 0.96) for identifying hemodynamically significant stenoses compared to invasive FFR.
Why the study?
Does a simplified model of FFR calculation (FFRsim) derived from 3D coronary angiography accurately predict standard invasive FFR measurements in vessels with a single stenosis?
Observational (n=64)
Yes
Does a simplified model of FFR calculation (FFRsim) derived from 3D coronary angiography accurately predict standard invasive FFR measurements in vessels with a single stenosis?
Effect estimate: AUC 0.96 (95% CI 0.91-1)
A simplified 3D angiography-derived FFR model (FFRsim) demonstrates high diagnostic accuracy compared to invasive FFR, potentially allowing the omission of invasive pressure wire measurements in a majority of cases.
Supports prospective validation of FFRsim; should not yet replace invasive FFR in practice.
AIMS: The aim of this study was to develop a simplified model of FFR calculation (FFRsim) derived from three-dimensional (3D) coronary angiographic data and classic fluid dynamic equations without using finite element analysis. METHODS AND RESULTS: Intracoronary pressure measurements were performed by pressure wire sensors. The lumens of the interrogated vessel segments were reconstructed in 3D. The coronary artery volumetric flow was calculated based on the velocity of the contrast material. Pressure gradients were computed by classic fluid dynamic equations. The diagnostic power of the simplified computation of the FFR (FFRsim) was assessed by comparing the results with standard invasive FFR measurements (FFRmeas) in 68 vessels with a single stenosis. We found a strong correlation between the FFRsim and the FFRmeas (r=0.86, p<0.0001). The sensitivity and specificity for predicting the abnormal FFR of ≤0.80 (indicating haemodynamically significant stenosis) were 90% and 100%, respectively. The area under the curve (AUC) was 0.96. To achieve 100% negative and positive predictive values we defined the FFRsim >0.88 and the FFRsim ≤0.8 ranges. In our patient population, these ranges were found in 69% of the cases. CONCLUSIONS: According to our simplified model, the invasive FFR measurement can be omitted without misclassification in pre-specified ranges of the calculated FFRsim.
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Tar et al. (2018) conducted an observational in Intermediate severity epicardial coronary artery disease (n=64). FFRsim (calculated fractional flow reserve) vs. FFRmeas (invasive fractional flow reserve) was evaluated on Diagnostic accuracy (AUC) for predicting abnormal FFR ≤0.80 (AUC 0.96, 95% CI 0.91-1). The simplified FFRsim model derived from 3D coronary angiography demonstrated high diagnostic accuracy (AUC 0.96) for identifying hemodynamically significant stenoses compared to invasive FFR.
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