This deliverable demonstrates how the sensitivity analyses described in D7.4 - Sensitivity and uncertainty quantification toolbox (TUE, M15) can be used to re-define the input space. Three possible objectives regarding input space re-definition are considered: 1) reducing the number of parameters that span the input space (i.e., dimensionality reduction by parameter fixing) to facilitate amongst other physiological model development and future model output uncertainty quantification; 2) reducing the input uncertainty domains of the model parameters, boundary conditions and/or model assumptions that will lead to the most significant reduction in model output uncertainty (parameter prioritisation); 3) defining the regions of the input space that result in parameter sets that lead to physiologically realistic realisations of the virtual cohort generator. This deliverable starts with a summary of the most important definitions and background of the uncertainty and sensitivity analyses discussed in D7.4. Thereafter, we will show how these sensitivity analysis techniques are applied to address the three different objectives above. In this deliverable three models, possible candidates for virtual cohort generation of the SIMCor use cases (i.e., aortic valve stenosis and heart failure patients), are selected as typical examples. Finally, the future steps towards virtual cohort generation and validation are discussed.

SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.