Impact of baseline coronary flow and its distribution on fractional flow reserve prediction

• Published in: International journal for numerical methods in biomedical engineering 2021-11, Vol.37 (11), p.e3246-n/a
• Main Authors: Müller, Lucas O., Fossan, Fredrik E., Bråten, Anders T., Jørgensen, Arve, Wiseth, Rune, Hellevik, Leif R.
• Format: Article
• Language: English
• Subjects: Cardiovascular disease; Coronary artery; Coronary artery disease; coronary flow; Drugs; fractional flow reserve; Heart diseases; Prediction models; reduced‐order model; Stenosis; Three dimensional models
• ISSN: 2040-7939; 2040-7947
• DOI: 10.1002/cnm.3246

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. We explore the impact of several methods available in the literature to estimate and distribute baseline coronary flow on fractional flow reserve (FFR) predictions obtained with a reduced‐order model and show that they can 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 hyperemia have to be addressed in order to improve FFR prediction accuracy.