Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance.

Abstract

Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFR_ML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFR_ML simulation was about 10 min. On a per-vessel basis, the FFR_ML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFR_ML was 0.984. In this selected cohort of patients, the FFR_ML improves the computational efficiency and ensures the accuracy. The favorable performance of FFR_ML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.