The synthetic cardiovascular digital twin generated a virtual patient database of 34,347 patients reproducing systolic aortic pressure of 116.8 ± 16.11 mmHg closely matching literature value of 113.0 ± 11.2 mmHg.
A synthetic virtual patient database of the cardiovascular system was successfully developed to generate physiologically realistic waveforms and demographic variability, providing a foundation for in silico clinical trials and machine learning applications.
Absolute Event Rate: 116.8% vs 113%
Abstract Purpose The goal is to develop a cardiovascular virtual patient database (VPD) combining physiological and demographic data to provide the foundation for future applications in medical diagnostics, decision-making, credibility testing, and formal uncertainty analysis, and to enable its integration with three-dimensional (3D) hemodynamic models and to train neural networks. Methods We generate an initial VPD by treating input parameters of a low-dimensional cardiovascular model as stochastic variables. Literature data and sensitivity analysis ensured physiological plausibility, while resampling improved physiological accuracy. Key physiological quantities are included such as systolic and diastolic aortic pressure, radial and carotid pressure, cardiac output, and diagnostic pulse wave velocities. Demographic factors (sex and age) are assigned based on their physiological impact. The open-source hemodynamic solver, firstblood, ensures accuracy and low computational time. Results The initial VPD consists of 50, 000 Virtual Patients; after resampling, 34, 347 remain in the final VPD. The difference of diastolic and systolic aortic pressures between the VPD (70. 24 14. 3 70. 24 ± 14. 3 and 116. 8 16. 11 116. 8 ± 16. 11 mmHg) and the literature (75. 6 12. 7 75. 6 ± 12. 7 and 113. 0 11. 2 113. 0 ± 11. 2 mmHg) is low. The differences caused by the sex of the patient are reproduced well by the VPD: increased diastolic aortic pressure for males (72. 1 12. 6 72. 1 ± 12. 6 and 68. 5 15. 4 68. 5 ± 15. 4 for males and females respectively). The VPD also accurately includes higher pulse wave velocities with age, patients below year 30 have 6. 3 0. 5 6. 3 ± 0. 5 and above 70 have 8. 8 1. 2 8. 8 ± 1. 2 m/s with a linear increment in-between. Conclusions The proposed methodology and firstblood solver effectively generate physiologically realistic virtual patient waveforms and demographic variability, providing a robust database for 3D cardiovascular simulations, machine-learning training datasets, and potential clinical decision support applications
Wéber et al. (Thu,) conducted a other in synthetic virtual patients representing the human cardiovascular system with assigned demographic data (sex and age) (n=34,347). Generation of synthetic virtual patient database using a cardiovascular digital twin model vs. None (simulation vs literature data) was evaluated on Agreement of physiological quantities of interest (QOIs) including systolic and diastolic aortic pressures, radial and carotid pressures, cardiac output, and pulse wave velocities (PWVs) with literature references. The synthetic cardiovascular digital twin generated a virtual patient database of 34,347 patients reproducing systolic aortic pressure of 116.8 ± 16.11 mmHg closely matching literature value of 113.0 ± 11.2 mmHg.