Influence of Input Data Composition and Measurement Errors on Computational Model-Mediated Assessment of...

Area of Science:

• Cardiovascular physiology
• Computational modeling
• Biomedical engineering

Background:

• Computational cardiovascular models integrated with clinical data offer quantitative assessment potential.
• Sensitivity to input data composition and measurement errors in these models is not well understood.

Purpose of the Study:

• To evaluate the impact of clinical data variations and errors on the accuracy of computational cardiovascular model parameter estimation.
• To determine the optimal input data requirements for reliable cardiovascular assessment using computational models.

Main Methods:

• Generated in silico cardiovascular datasets representing diverse virtual subjects.
• Used hemodynamic data (brachial blood pressure, aortic/peripheral flow velocities) as input for the Levenberg-Marquardt parameter estimation algorithm.
• Assessed parameter estimation accuracy against ground truth model parameters (arterial stiffness, cardiac function, vascular resistance).

Main Results:

• Accurate parameter estimation was achieved with brachial blood pressure and flow velocities from the ascending aorta and four peripheral arteries.
• Model performance decreased significantly with the omission of flow velocity data from two peripheral arteries.
• Introduction of measurement errors in input data substantially increased errors in parameter estimation and hemodynamic prediction.

Conclusions:

• Rigorous selection of clinical data is crucial for reliable computational model-mediated cardiovascular assessment.
• High-precision in vivo measurements are essential to minimize errors and enhance the accuracy of quantitative cardiovascular assessments.
• The number and location of peripheral flow velocity measurements impact the reliability of cardiovascular model parameter estimation.