Prediction of fractional flow reserve based on reduced-order cardiovascular model

Abstract

Fractional flow reserve (FFR) is the “gold standard” for diagnosis of myocardial ischemia. The existing 3D computational fluid dynamics (CFD) algorithm based on coronary computed tomography angiography (CTA) has realized the non-invasive calculation of FFR (FFRCT), but the CFD simulation in 3D coronary arteries is time consuming. The purpose of this study was to develop a reduced-order (0D) cardiovascular model for rapid computation of pressure and flow through coronary arteries and stenosis and to ensure accuracy in prediction of FFR (FFR0D). One hundred patients (118 lesions) who had undergone invasive FFR were enrolled retrospectively. Based on coronary CTA, a patient-specific lumped parameter model of the cardiovascular system and coronary system was constructed. In addition, an experimentally validated, theoretical calculation model of stenosis resistance was introduced. Stenosis resistance and flow rate were quantified by coupling the two models, and FFR0D was calculated according to the coronary pressure distribution. The overall modeling time of FFR0D was approximately 41.2 min while FFRCT was reported to be 3.5 h. Using the clinical cut-off value of FFR (0.8), the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of FFR0D were 89.8%, 79.2%, 92.6%, 73.1% and 94.6%, respectively, while that of FFRCT were reported to be 84.3%, 87.9%, 82.2%, 73.9% and 92.2%, respectively. We successfully proposed a reduced-order cardiovascular model for rapid and accurate prediction of FFR. Compared with FFRCT, FFR0D reduced computational cost and ensured computational accuracy in the case of our limited samples. These advantages represent an advance in the diagnosis and treatment of myocardial ischemia.