Establishing Hemodynamic Convergence Framework for Coronary Digital Twins Under Realistic Dynamic Heart Rates

Area of Science:

• Cardiovascular physiology
• Computational fluid dynamics
• Digital twin technology

Background:

• Digital twins are crucial for patient-specific cardiovascular modeling, but simulating dynamic heart rate changes presents challenges.
• Accurate simulations require understanding temporal convergence and the role of preceding cardiac cycles ('pre-flows').
• Existing methods struggle with real-world transitions like those from physical activity.

Purpose of the Study:

• To establish a physiologically grounded method for determining the minimum number of pre-flows for temporal convergence after abrupt heart rate changes.
• To quantify convergence behavior across key hemodynamic parameters (velocity, pressure gradient, wall shear stress).
• To validate the findings using both synthetic and real-world wearable-derived heart rate data.

Main Methods:

• High-resolution patient-specific 3D simulations were performed.
• Inflow waveforms were scaled using synthetic and wearable-derived heart rate data.
• Convergence was analyzed at cross-sectional and full-domain levels.

Main Results:

• Two pre-flows were sufficient to achieve stable hemodynamic outputs (<2% difference) during high-to-low and low-to-high heart rate transitions.
• Findings were robustly verified using continuous heart rate data from wearable devices (<1% difference).
• The proposed convergence criterion demonstrated consistency across various dynamic heart rate scenarios.

Conclusions:

• A computationally efficient and physiologically consistent criterion for dynamic-state cardiovascular simulations has been established.
• This criterion facilitates the integration of cardiovascular digital twins with real-time sensing technologies.
• The study provides a practical approach for improving the accuracy and applicability of cardiovascular digital twins in clinical settings.