Key result
A novel computational approach successfully generated a virtual population of 10,000 patients with coronary artery disease, demonstrating good agreement with real clinical data (mean goodness of fit 0.1).
Why the study?
To develop a cardiovascular virtual population using statistical modeling and computational biomechanics for in silico clinical trials of stent design.
A novel computational methodology successfully generated a realistic virtual population of 10,000 patients with 3D coronary arterial geometries, enabling future in silico clinical trials for stent design.
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May accelerate in silico stent trials in CAD; leaves open prospective clinical validation.
Goal: To develop a cardiovascular virtual population using statistical modeling and computational biomechanics. Methods: A clinical data augmentation algorithm is implemented to efficiently generate virtual clinical data using a real clinical dataset. An atherosclerotic plaque growth model is employed to 3D reconstructed coronary arterial segments to generate virtual coronary arterial geometries (geometrical data). Last, the combination of the virtual clinical and geometrical data is achieved using a methodology that allows for the generation of a realistic virtual population which can be used in in silico clinical trials. Results: The results show good agreement between real and virtual clinical data presenting a mean gof 0.1 ± 0.08. 400 virtual coronary arteries were generated, while the final virtual population includes 10,000 patients. Conclusions: The virtual arterial geometries are efficiently matched to the generated clinical data, both increasing and complementing the variability of the virtual population.
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Pleouras et al. (2021) studied Coronary Artery Disease (n=186). Virtual population generation using statistical modeling and computational biomechanics vs. Real clinical dataset was evaluated on Goodness of fit (gof) between real and virtual clinical data. A novel computational approach successfully generated a virtual population of 10,000 patients with coronary artery disease, demonstrating good agreement with real clinical data (mean goodness of fit 0.1).
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