Central arterial pressure and patient-specific model parameter estimation based on radial pressure measurements

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Computational Fluid Dynamics

Background:

• Estimating central aortic pressure from peripheral measurements is crucial for cardiovascular assessment.
• Existing methods may lack precision or require invasive procedures.
• Patient-specific arterial network modeling offers a non-invasive approach.

Purpose of the Study:

• To develop and validate a novel method for estimating patient-specific aortic pressure curves.
• To reduce the complexity of system parameters in one-dimensional arterial flow simulations.
• To utilize radial applanation tonometry for non-invasive pressure estimation.

Main Methods:

• Implementation of one-dimensional arterial flow simulations.
• Development of a novel parameter reduction and calculation method.
• Optimization based on simultaneously measured peripheral and aortic pressure curves.
• Validation using patient-specific arterial networks.

Main Results:

• The developed method accurately reproduced patient-specific aortic pressure curves.
• Average overestimation for Systolic Blood Pressure was 0.6 mmHg.
• Average overestimation for Mean Blood Pressure was 0.5 mmHg.
• Average Root Mean Square Difference between measured and estimated curves was 3 mmHg.

Conclusions:

• The novel method provides a reliable and accurate non-invasive estimation of central aortic pressure.
• Reduced system parameters enhance the efficiency of one-dimensional arterial flow simulations.
• This approach holds promise for improved patient-specific cardiovascular monitoring.