A zero-dimensional predictive model for the pressure drop in the stenotic coronary artery based on its geometric characteristics

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

BACKGROUND AND OBJECTIVE

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.

METHODS

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.

RESULTS

A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML 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 FFRML was 0.984.

CONCLUSION

In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

A novel physics-based model for fast computation of blood flow in coronary arteries

Blood flow and pressure calculated using the currently available methods have shown the potential to predict the progression of pathology, guide treatment strategies and help with postoperative recovery. However, the conspicuous disadvantage of these methods might be the time-consuming nature due to the simulation of virtual interventional treatment. The purpose of this study is to propose a fast novel physics-based model, called FAST, for the prediction of blood flow and pressure. More specifically, blood flow in a vessel is discretized into a number of micro-flow elements along the centerline of the artery, so that when using the equation of viscous fluid motion, the complex blood flow in the artery is simplified into a one-dimensional (1D) steady-state flow. We demonstrate that this method can compute the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA). 345 patients with 402 lesions are used to evaluate the feasibility of the FAST simulation through a comparison with three-dimensional (3D) computational fluid dynamics (CFD) simulation. Invasive FFR is also introduced to validate the diagnostic performance of the FAST method as a reference standard. The performance of the FAST method is comparable with the 3D CFD method. Compared with invasive FFR, the accuracy, sensitivity and specificity of FAST is 88.6%, 83.2% and 91.3%, respectively. The AUC of FFR_FAST is 0.906. This demonstrates that the FAST algorithm and 3D CFD method show high consistency in predicting steady-state blood flow and pressure. Meanwhile, the FAST method also shows the potential in detecting lesion-specific ischemia.

The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease

It was hypothesized that the microcirculatory resistance of resting state (R_m-res) might be a good predictor for ischemia. In this study, the quantitative relationship between R_m-res and myocardial ischemia in different stenosed degrees was explored and verified through retrospective analysis, and the diagnostic performance was evaluated. 136 patients were screened and divided into a training set (90 patients) and a validation set (46 patients). In the training set, R_m-res was calculated, and thresholds were determined by exploring the relationship between R_m-res and myocardial ischemia in different stenosed degrees. In the validation set, the diagnostic performance of the thresholds was verified. It was found that the 90 data mean difference (95%CI) of R_m-res between the ischemic group and the non-ischemic group was 63.03 (95 %CI: 25.72-100.34), p < 0.05. In the training set with stenosed degree 41-60%, 61-70%, 71-80%, and >81%, the average of R_m-res in the ischemic and non-ischemic groups were (80.79, 136.87), (96.41, 172.62), (128.99, 198.94) and (175.95, 310.79) mmHg/s/ml. The R_m-res thresholds were 87.18, 118.96, 142.35, and 177.39 mmHg/s/ml. In the validation set, the overall sensitivity, specificity, PPV, NPV, and accuracy were 73.3%, 77.4%, 61.1%, 85.7%, and 76.1%. In conclusion, R_m-res had a significant predictor on myocardial ischemia. As a smaller R_m-res represents greater myocardial mass perfusion, it is more likely that a stenosis will have a functional impact. Threshold analysis showed that R_m-res of different stenosed degrees was a quantitative predictor of myocardial ischemia, which could assist physicians with clinical treatment strategies.

An improved reduced-order model for pressure drop across arterial stenoses

Quantification of pressure drop across stenotic arteries is a major element in the functional assessment of occlusive arterial disease. Accurate estimation of the pressure drop with a numerical model allows the calculation of Fractional Flow Reserve (FFR), which is a haemodynamic index employed for guiding coronary revascularisation. Its non-invasive evaluation would contribute to safer and cost-effective diseases management. In this work, we propose a new formulation of a reduced-order model of trans-stenotic pressure drop, based on a consistent theoretical analysis of the Navier-Stokes equation. The new formulation features a novel term that characterises the contribution of turbulence effect to pressure loss. Results from three-dimensional computational fluid dynamics (CFD) showed that the proposed model produces predictions that are significantly more accurate than the existing reduced-order models, for large and small symmetric and eccentric stenoses, covering mild to severe area reductions. FFR calculations based on the proposed model produced zero classification error for three classes comprising positive (≤ 0.75), negative (≥ 0.8) and intermediate (0.75 − 0.8) classes.