Key result
Methods to estimate and distribute baseline coronary flow significantly impacted diagnostic performance for FFR prediction, but none significantly improved prediction error standard deviation.
Why the study?
Model-based prediction of FFR in suspected stable CAD requires assumptions, including defining baseline coronary flow at rest, whose impact on predictions required exploration.
How do different methods of estimating and distributing baseline coronary flow impact the accuracy of reduced-order model-based FFR predictions in patients with suspected stable CAD?
Observational (n=63)
How do different methods of estimating and distributing baseline coronary flow impact the accuracy of reduced-order model-based FFR predictions in patients with suspected stable CAD?
Different methods for estimating baseline coronary flow significantly impact FFR prediction models, but improving prediction error requires addressing uncertainties in stenosis geometry and hyperemic drug effects.
Flow estimation methods alter FFR model diagnostics; leaves open whether geometry or hyperemia uncertainties limit further gains.
Model-based prediction of fractional flow reserve (FFR) in the context of stable coronary artery disease (CAD) diagnosis requires a number of modelling assumptions. One of these assumptions is the definition of a baseline coronary flow, ie, total coronary flow at rest prior to the administration of drugs needed to perform invasive measurements. Here we explore the impact of several methods available in the literature to estimate and distribute baseline coronary flow on FFR predictions obtained with a reduced-order model. We consider 63 patients with suspected stable CAD, for a total of 105 invasive FFR measurements. First, we improve a reduced-order model with respect to previous results and validate its performance versus results obtained with a 3D model. Next, we assess the impact of a wide range of methods to impose and distribute baseline coronary flow on FFR prediction, which proved to have a significant impact on diagnostic performance. However, none of the proposed methods resulted in a significant improvement of prediction error standard deviation. Finally, we show that intrinsic uncertainties related to stenosis geometry and the effect of hyperemic inducing drugs have to be addressed in order to improve FFR prediction accuracy.
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Müller et al. (2019) conducted an observational in Suspected stable coronary artery disease (n=63). Reduced-order model for FFR prediction vs. Invasive FFR measurements was evaluated on FFR prediction error standard deviation. Methods to estimate and distribute baseline coronary flow significantly impacted diagnostic performance for FFR prediction, but none significantly improved prediction error standard deviation.
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