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

Feng, Yixiang; Fu, Ruisen; Li, Bao; Li, Na; Yang, Haisheng; Liu, Jian; Liu, Youjun

doi:10.1016/j.cma.2022.115473

Cited by 8 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reduced-order models of coronary circulation provide a powerful tool for diagnostics. 1D [15,17], 0D [19] and combined 1D-0D [14] approaches allow for a fast computations with adequate precision. The choice between 1D and 0D models is usually made based on the degree of flow detail required.…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis of Hemodynamic Indices in Multivessel Coronary Artery Disease in the Presence of Myocardial Perfusion Dysfunction

Gamilov,

Danilov,

Chomakhidze

et al. 2024

Computation

View full text Add to dashboard Cite

Coronary artery disease (CAD) is one of the main causes of death in the world. Functional indices such as fractional flow reserve (FFR), coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) are used to estimate the severity of CAD. Approximately 30–50% of patients have residual myocardial ischaemia even after formally successful percutaneous coronary intervention (PCI). Myocardial perfusion impairment is one of the main factors responsible for recurrence. We propose a novel 1D model of coronary hemodynamics that takes into account myocardial contraction, stenoses and impaired microcirculation. It uses non-invasively acquired data. The model is able to simulate FFR and iFR with a mean relative error of 3% and a standard mean deviation of 0.04. We find that healthy FFR and iFR values in the short and long term do not always correspond to healthy CFR values and recovery of coronary blood flow. We also show that PCI of stenosis also improves hemodynamic indices in adjacent stenosed vessels, with a more pronounced effect in the long term.

show abstract

Section: Introductionmentioning

confidence: 99%

Computational Analysis of Hemodynamic Indices in Multivessel Coronary Artery Disease in the Presence of Myocardial Perfusion Dysfunction

Gamilov,

Danilov,

Chomakhidze

et al. 2024

Computation

View full text Add to dashboard Cite

show abstract

“…However, the ratio (Q LCA :Q RCA = 6:4) of splitting flow of the left/right coronary artery was obtained statistically. Many studies [14][15][16][17] have used the method of calculating FFRct by Taylor et al In addition, vessel diameter has an impact on flow rate. Thus, diameter was used in this study to determine the ratio when splitting flow to the left/right in different dominance types.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Liu

et al. 2023

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Numerical modeling is being used to support clinical studies to complement patient data. 0D CVS‐VAD models have been used to evaluate commercially available LVADs, 3 study the effect of speed changes during exercise, 4 and for non‐invasive, rapid, and accurate diagnosis of myocardial ischemia 5 . Another potential application of cardiovascular modeling is hemodynamic ramp testing (HRT), an invasive procedure that investigates the feasibility of LVAD speed adjustments to optimize a patient's hemodynamic profile, enhance life quality post‐implant, and guide patient management 6 .…”

Section: Introductionmentioning

confidence: 99%

In‐silico hemodynamic ramp testing of ventricular assist device implanted patients using acausal cardiovascular‐VAD modeling

et al. 2023

View full text Add to dashboard Cite

BackgroundWhile cardiovascular system and mechanical circulatory support devices are efficiently model the effect of disease and assistance, they can also lend valuable insights into clinical procedures. This study demonstrates the use of a CVS‐VAD model for an invasive procedure; hemodynamic ramp testing, in‐silico.MethodsThe CVS model is developed using validated models in literature, using Simscape™. An analytically derived pump model is calibrated for the HeartWare VAD. Dilated cardiomyopathy is used as an illustrative example of heart failure, and heart failure patients are created virtually by calibrating the model with requisite disease parameters obtained from published patient data. A clinically applied ramp study protocol is adopted whereby speed optimization is performed following clinically accepted hemodynamic normalization criteria. Trends in hemodynamic variables in response to pump speed increments are obtained. Optimal speed ranges are obtained for the three virtual patients based on target values of central venous pressure (CVP), pulmonary capillary wedge pressure (PCWP), cardiac output (CO), and mean arterial pressure (MAP) for hemodynamic stabilization.ResultsAppreciable speed changes in the mild case (300 rpm), slight changes in the moderate case (100 rpm), and no changes in the simulated severe case are possible.ConclusionThe study demonstrates a novel application of cardiovascular modeling using an open‐source acausal model, which can be potentially beneficial for medical education and research.

show abstract

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

Cited by 8 publications

References 46 publications

Computational Analysis of Hemodynamic Indices in Multivessel Coronary Artery Disease in the Presence of Myocardial Perfusion Dysfunction

Computational Analysis of Hemodynamic Indices in Multivessel Coronary Artery Disease in the Presence of Myocardial Perfusion Dysfunction

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

In‐silico hemodynamic ramp testing of ventricular assist device implanted patients using acausal cardiovascular‐VAD modeling

Contact Info

Product

Resources

About