Impact of Arrhythmia on Myocardial Perfusion: A Computational Model-Based Study

Coronary artery disease is the leading cause of mortality worldwide, accounting for 12.8% of all deaths. Although the clinical benefits of treating stenosis with percutaneous coronary intervention (PCI) have been extensively demonstrated, residual myocardial ischemia remains in about 30–50% of patients even after a formally successful PCI. We apply previously developed and validated 1D model of haemodynamics, which distributes terminal hydraulic resistance based on the diameters of the parent vessels and Murray’s law by a recursive algorithm. In our new model the terminal resistance is decreased according to a transmural perfusion ratio increase. In contrast to our previous work we calculate the transmural perfusion ratio for personally defined zones. Thus, peripheral hydraulic resistance of myocardial perfusion is personalized based on patient data, whichwere extracted from computed tomography perfusion images. The model serves as a computational tool for simulating pre- to post-PCI changes in coronary haemodynamics of four patients. We simulate fractional flow reserve (FFR), coronary flow reserve (CFR), instantaneous wave-free ratio (iFR), average flow in selected arteries in hyperemic and rest conditions before PCI and after PCI immediately after the surgery (in a short-term) and in a long-term (several months) perspectives. We conclude that high FFR and iFR values in short-term and long-term perspectives are not necessary correlate with CFR improvement and long-term blood flow recovery in coronary arteries.

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Aortic Pressure Control Based on Deep Reinforcement Learning for Ex Vivo Heart Perfusion

Wang,

Yang,

Liu

et al. 2024

Applied Sciences

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In ex vivo heart perfusion (EVHP), the control of aortic pressure (AoP) is critical for maintaining the heart’s physiologic aerobic metabolism. However, the complexity of and variability in cardiac parameters present a challenge in achieving the rapid and accurate regulation of AoP. In this paper, we propose a method of AoP control based on deep reinforcement learning for EVHP in Langendorff mode, which can adapt to the variations in cardiac parameters. Firstly, a mathematical model is developed by coupling the coronary artery and the pulsatile blood pump models. Subsequently, an aortic pressure control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. This method enables the regulation of the blood pump and the realization of closed-loop control. The control performance of the proposed DDPG method, the traditional proportional–integral–derivative (PID) method, and the fuzzy PID method are compared by simulating single and mixed changes in mean aortic pressure target values and coronary resistance. The proposed method exhibits superior performance compared to the PID and fuzzy PID methods under mixed factors, with 68.6% and 66.4% lower settling times and 70.3% and 54.1% lower overshoot values, respectively. This study demonstrates that the proposed DDPG-based method can respond more rapidly and accurately to different cardiac conditions than the conventional PID controllers.

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Impact of Arrhythmia on Myocardial Perfusion: A Computational Model-Based Study

Cited by 3 publications

References 34 publications

A Web-Based Non-invasive Estimation of Fractional Flow Reserve (FFR): Models, Algorithms, and Application in Diagnostics

A Web-Based Non-invasive Estimation of Fractional Flow Reserve (FFR): Models, Algorithms, and Application in Diagnostics

Personalized computational estimation of relative change in coronary blood flow after percutaneous coronary intervention in short-term and long-term perspectives

Aortic Pressure Control Based on Deep Reinforcement Learning for Ex Vivo Heart Perfusion

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