Supplementary file 1_Accelerating cardiovascular digital twin creation using precomputed virtual populations as Bayesian priors.pdf

It subsequently applies weight-based allometric scaling of the resulting posterior parameter sets to more fully match target measurement variability. We applied this technique to a mechanistic hemodynamic model beginning with 23 parameters of interest. The final virtual population is then used to rapidly compute 17 proof-of-concept patient-specific digital twins, using patient data from an independent cohort not considered during virtual population development.</p>Results<p>Our methodology effectively replicated at least 74.6% of the expected coverage of the target measurement distributions, with 17 of 19 measurements being represented at more than 80% of the expected coverage.

Seventeen patient-specific digital twins were then computed from within this virtual population, with mean absolute percentage error across measurements for each patient ranging from 0.71% to 8.61% relative to the clinical measurement targets and a mean error across patients of less than 3%.</p>Conclusion<p>The proposed methodology represents a promising approach to developing a virtual population and demonstrates the feasibility of rapid posterior reweighting within such a precomputed virtual population, according to individual patient data.

This computational proof-of-concept may help set the stage for future scalable creation of patient-specific digital twins to aid in clinical decision-making.</p>