Computational Analysis of Haemodynamic Indices in Synthetic Atherosclerotic Coronary Netwroks

Simakov, Sergey; Gamilov, Timur; Kopylov, Philipp; Liang, Fuyou

doi:10.20944/preprints202108.0026.v1

2021

DOI: 10.20944/preprints202108.0026.v1

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Computational Analysis of Haemodynamic Indices in Synthetic Atherosclerotic Coronary Netwroks

Sergey Simakov¹,

Timur Gamilov²,

Philipp Kopylov³

et al.

Abstract: Haemodynamic indices are widely used in clinical practice for deciding on a particular type of treatment. Low quality of the CT data and tachycardia complicate interpretation of the measured or simulated values. In this work, we present a novel approach for evaluating resistances in terminal coronary arteries. Using 14 measurements from 10 patients, we show that this algorithm retains the accuracy of 1D haemodynamic simulations in less detailed (truncated) geometric models of coronary networks. We also apply t… Show more

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Cited by 3 publications

References 35 publications

(62 reference statements)

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

Gamilov,

Danilov,

Chomakhidze

et al. 2024

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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.

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

Gamilov,

Danilov,

Chomakhidze

et al. 2024

Computation

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Fractional-Order Windkessel Boundary Conditions in a One-Dimensional Blood Flow Model for Fractional Flow Reserve (FFR) Estimation

Gamilov

Yanbarisov

2023

Fractal Fract

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Recent studies have demonstrated the benefits of using fractional derivatives to simulate a blood pressure profile. In this work we propose to combine a one-dimensional model of coronary blood flow with fractional-order Windkessel boundary conditions. This allows us to obtain a greater variety of blood pressure profiles for better model personalization An algorithm of parameter identification is described, which is used to fit the measured mean value of arterial pressure and estimate the fractional flow reserve (FFR) for a given patient. The proposed framework is used to investigate sensitivity of mean blood pressure and fractional flow reserve to fractional order. We demonstrate that the fractional derivative order significantly affects the fractional flow reserve (FFR), which is used as an indicator of stenosis significance.

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Computational Analysis of Hemodynamic Indices Based on Personalized Identification of Aortic Pulse Wave Velocity by a Neural Network

et al. 2023

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Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16%. We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant (7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4% to 3.8% on average, with a maximum difference of 2.8% in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds.

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Computational Analysis of Haemodynamic Indices in Synthetic Atherosclerotic Coronary Netwroks

Cited by 3 publications

References 35 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

Fractional-Order Windkessel Boundary Conditions in a One-Dimensional Blood Flow Model for Fractional Flow Reserve (FFR) Estimation

Computational Analysis of Hemodynamic Indices Based on Personalized Identification of Aortic Pulse Wave Velocity by a Neural Network

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