Model-based cardiovascular disease diagnosis: a preliminary in-silico study.

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Medical Diagnostics

Background:

• Cardiovascular diseases are often diagnosed using indirect measures.
• Arterial mechanical properties are altered by cardiovascular diseases.
• Current diagnostic methods have limitations in sensitivity and convenience.

Purpose of the Study:

• To develop and assess a model-based system identification approach for cardiovascular disease diagnosis.
• To investigate the feasibility of diagnosing cardiovascular disease from arterial mechanical property alterations.
• To identify disease-specific patterns in arterial model parameters.

Main Methods:

• Individualized a lumped-parameter model of arterial wave propagation and reflection.
• Utilized proximal and distal arterial blood pressure waveforms for model calibration.
• Employed disease-specific patterns in model parameters ([Formula: see text] and pulse transit time) for diagnosis.

Main Results:

• The model parameters showed significant changes in response to simulated peripheral artery disease (up to 100% and 40%) and arterial stiffening (up to 300% and 40%).
• The approach demonstrated superior sensitivity compared to the ankle-brachial index.
• The model parameters responded more sensitively than carotid-femoral pulse wave velocity.

Conclusions:

• The proposed model-based system identification approach is feasible for diagnosing cardiovascular diseases.
• This method offers a potentially viable alternative to current state-of-the-art diagnostic techniques.
• Further development alongside convenient blood pressure waveform measurement could enhance clinical adoption.