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Sensitivity analysis and optimization of a cardiovascular lumped parameter model for patient-specific modelling
Siti Munirah Muhammad Ali1,2, Wahbi El-Bouri3, Wan Naimah Wan Ab Naim1
1Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
Summary
Accurate patient-specific cardiovascular models are crucial. This study improved parameter estimation using sensitivity analysis and genetic algorithms, enhancing model accuracy for mean arterial pressure (MAP) prediction.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Cardiovascular Systems
Background:
- Accurate patient-specific cardiovascular models are essential for understanding and treating cardiovascular diseases.
- Parameter estimation in these complex models presents a significant challenge, impacting their clinical applicability.
- Lumped parameter models offer a simplified yet effective approach to cardiovascular simulation.
Purpose of the Study:
- To develop and validate a framework for enhanced parameter estimation in patient-specific lumped parameter cardiovascular models.
- To improve the accuracy of cardiovascular model outputs, specifically mean arterial pressure (MAP).
- To leverage sensitivity analysis and multi-objective genetic algorithms for robust parameter optimization.
Main Methods:
- Implemented a framework combining sensitivity analysis for parameter identification with multi-objective genetic algorithm optimization.
- Identified and optimized four key influential parameters within the lumped parameter cardiovascular model.
- Validated model outputs, particularly mean arterial pressure (MAP), against clinical data from a public database.
Main Results:
- The optimized model demonstrated a highly significant correlation between simulated and clinical mean arterial pressure (MAP) (r = 0.99997, p < 0.001).
- Statistical equivalence between the model's MAP and clinical MAP was confirmed using a t-test (p = 0.752).
- Sensitivity analysis successfully identified the most influential parameters for optimization.
Conclusions:
- The proposed framework significantly enhances parameter estimation accuracy in patient-specific cardiovascular models.
- The combination of sensitivity analysis and genetic algorithms provides a powerful tool for optimizing lumped parameter models.
- This approach holds substantial potential for improving the reliability and clinical utility of patient-specific cardiovascular simulations.
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