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

A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model

The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards non-invasive prediction of FFR. Although the non-invasive derivation of FFR (FFRCT) using computational fluid dynamics (CFD) principles has become a common practice, its clinical application has been limited due to the considerable time required for computation of resulting changes in haemodynamic conditions. An alternative to CFD technology is incorporating a neural network into the computational process to reduce the time necessary for running an effective model. In this study we propose a cascade of data-driven and physic-based neural networks (DP-NN) for predicting FFR (DL-FFRCT). The first network of cascade network DP-NN includes geometric features, and the second network includes physical features. We compare the differences between data-driven neural network (D-NN) and DP-NN for predicting FFR. The training and testing datasets were obtained by solving the three-dimensional incompressible Navier-Stokes equations. Coronary flow and geometric features were used as inputs to train D-NN. In DP-NN the training process involves first training a D-NN to output resting ΔP as one input feature to the DP-NN. Secondly, the physics-based microcirculatory resistance as another input feature to the DP-NN. Using clinically measured FFR as the "gold standard", we validated the computational accuracy of DL-FFRCT in 77 patients. Compared to D-NN, DP-NN improved the prediction of ΔP (R2 = 0.87 vs. R2 = 0.92). Statistical analysis demonstrated that the diagnostic accuracy of DL-FFRCT was not inferior to FFRCT (85.71 % vs. 88.3 %) and the computational time was reduced by a factor of approximately 3000 (4.26 s vs. 3.5 h). DP-NN represents a near real-time, interpretable, and highly accurate deep-learning network, which contributes to the development of high-performance computational methods for haemodynamics. We anticipate that DP-NN will enable near real-time prediction of DL-FFRCT in personalized narrow blood vessels and provide guidance for cardiovascular disease treatments.

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis

BACKGROUND AND OBJECTIVE

The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis.

METHODS

We combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR.

RESULTS

The average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively.

CONCLUSIONS

The proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

Left and right coronary artery blood flow distribution method based on dominant type

The purpose of the current study was to investigate the effects of left/right coronary artery flow distribution on calculation of fractional flow reserve derived from coronary computed tomography angiography (FFRct) in different dominant types. First, 195 patients were collected to count the distribution ratios of the three categories: right dominance (RD), balanced dominance (BD), and left dominance (LD). Ratios of diameters of the left/right coronary arteries (DLCA :DRCA ) of the three types were calculated and used to represent the ratio of flow distribution (QLCA :QRCA ) in the dominant type method. The other method was known as the fixed ratio method (QLCA :QRCA  = 6:4). Second, a total of 73 patients with coronary artery disease (CAD) were enrolled for numerical calculation. A 0D/3D geometric multiscale model was used for the numerical simulation of FFR and the results of the fixed ratio method and the dominant type method were recorded as F-FFRct and D-FFRct. Lastly, invasive FFR(clinic-FFR)was used as a standard to evaluate the consistency and diagnostic performance of F-FFRct and D-FFRct. Corresponding flow distributions for the dominant type method were QLCA :QRCA  = 5:5 for RD, QLCA :QRCA  = 5.5:4.5 for BD, and QLCA :QRCA  = 6:4 for LD. D-FFRct showed a better correlation than F-FFRct (r = 0.85 vs. r = 0.81, both p < .001); the AUC (95%CI) were 0.974 (0.906-0.997, p < .0001) and 0.960 (0.886-0.992, p < .0001). Accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive values (NPV) for D-FFRct and F-FFRct were 94.52%, 93.75%, 94.74%, 83.33%, 98.18% and 90.41%, 87.50%, 91.23%, 73.68%, 96.30%, respectively. Overall, the left/right coronary artery flow distribution was affected by the dominant type and the dominant type method was superior to the fixed ratio method in detecting coronary ischemic lesions.

Research on individualized distribution approach of coronary resting blood flow for noninvasive calculation of fractional flow reserve

BACKGROUND AND OBJECTIVES

The distribution of coronary resting blood flow is critical for accurately calculating the computed tomography (CT) angiography-derived fractional flow reserve (FFRCT). However, the diagnostic accuracy of FFRCT calculated by the fixed exponents between two risk factors and coronary resting blood flow, including myocardial mass and diameter of the coronary artery branch, was insufficient compared with invasive fractional flow reserve (FFR). In this study, we proposed the individualized distribution of coronary resting blood flow based on coronary ultrasound blood flow measurement, to improve the diagnostic accuracy of FFRCT calculation.

METHODS

Five risk factors and an unknown coefficient K were integrated to calculate the individualized distribution of coronary resting blood flow. The K value was fit using the least square method based on coronary ultrasound blood flow measurement results of 30 volunteers. We developed a novel approach for calculating the individualized distribution of coronary resting blood flow and applied it to calculate FFRCT (FFRCTI). Then, we tested the performance of the individualized distribution approach by comparing it with the approach proposed by Taylor based on coronary ultrasound blood flow measurement results of 13 volunteers. Finally, we tested the diagnostic accuracy of FFRCT calculated by two approaches in invasive FFR of 121 vessels with coronary stenosis.

RESULTS

We identified five risk factors and 6.83×10-5 for K value, including cardiac output, mean arterial pressure, myocardial mass, coronary artery volume, and diameter of the coronary artery branch, to calculate the individualized distribution of coronary resting blood flow. The mean square error of the individualized distribution approach (0.0088) was significantly less than that of the approach proposed by Taylor (0.0799). The diagnostic accuracy of FFRCTI calculated by the individualized distribution approach (91.74%) was higher than that of the approach proposed by Taylor (FFRCTT) (82.64%).

CONCLUSIONS

The individualized distribution approach of coronary resting blood flow can significantly improve the diagnostic accuracy of FFRCT calculation compared with invasive FFR, and support its wide clinical application for diagnosing myocardial ischemia caused by coronary stenosis.

A novel method for calculating CTFFR based on the flow ratio between stenotic coronary and healthy coronary

BACKGROUND

Epicardial coronary stenosis may lead to myocardial ischaemia, and the resulting obstructive coronary artery disease is one of the leading causes of death. CT-derived fractional flow reserve (CT-FFR) has been clinically shown to be an effective method for the noninvasive assessment of coronary artery stenosis. However, this method has the problem that the measurement result is affected by the selected measurement position.

OBJECTIVES

This study was to obtain a novel flow-based approach to coronary CTFFR (CTQFFR), which was not affected by the measurement location.

METHODS

This study established healthy-assumed coronary arteries based on narrowed coronary arteries. Based on the assumption that the microvascular resistance remains unchanged in the short term after coronary stenosis treatment, the blood flow in the stenotic coronary artery and the healthy-assumed coronary artery was obtained by numerical simulation, and the CTQFFR based on the blood flow ratio was calculated. The functional relationship between CTQFFR and FFR was fitted by the results of 20 cases.

RESULTS

In this study, the functional relationship between CTQFFR and FFR was fitted by a quadratic curve, and the variance was 0.8744; the functional relationship between CTQFFR and pressure-based approach to coronary CTFFR (CTPFFR) was fitted by a primary curve, and the variance was 0.9971. There was coronary artery growth in all 20 cases. Preliminary validation results using 10 cases showed 100% accuracy in determining whether coronary artery stenosis required for clinical intervention. The relative error of the coefficient with the results proposed in a previous study was 0.316%.

CONCLUSION

This study proposes a new method for calculating coronary CTFFR, namely, coronary CTQFFR, which is the flow ratio between stenotic coronary and healthy-assumed coronary. This method solves the problem that the downstream CTFFR of coronary stenosis is related to the selected location, which effectively improves the CTFFR at the critical value (CTFFR= 0.8) near reliability. Preliminary research results show that the method obtained in this study has a high accuracy for determining whether there is significant coronary stenosis. However, large multi-centre validation for the feasibility of this method was necessary in our future work.

The potential relationship of coronary artery disease and hyperuricemia: A cardiometabolic risk factor

Coronary arterial disease (CAD) is the leading cause of mortality in the world. Hyperuricemia has recently emerged as a novel independent risk factor of CAD, in addition to the traditional risk factors such as hyperlipidemia, smoking, and obesity. Several clinical studies have shown that hyperuricemia is strongly associated with the risk, progression and poor prognosis of CAD, as well as verifying an association with traditional CAD risk factors. Uric acid or enzymes in the uric acid production pathway are associated with inflammation, oxidative stress, regulation of multiple signaling pathways and the renin-angiotensin-aldosterone system (RAAS), and these pathophysiological alterations are currently the main mechanisms of coronary atherosclerosis formation. The risk of death from CAD can be effectively reduced by the uric acid-lowering therapy, but the interventional treatment of uric acid levels in patients with CAD remains controversial due to the diversity of co-morbidities and the complexity of causative factors. In this review, we analyze the association between hyperuricemia and CAD, elucidate the possible mechanisms by which uric acid induces or exacerbates CAD, and discuss the benefits and drawbacks of uric acid-lowering therapy. This review could provide theoretical references for the prevention and management of hyperuricemia-associated CAD.