Uncertainty in model-based treatment decision support: Applied to aortic valve stenosis.

ABSTRACT: Patient outcome in trans-aortic valve implantation (TAVI) therapy partly relies on a patient's haemodynamic properties that cannot be determined from current diagnostic methods alone. In this study, we predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis patients. A framework to incorporate uncertainty in patient-specific model predictions for decision support is presented. A 0D lumped parameter model including the left ventricle, a stenotic valve and systemic circulatory system has been developed, based on models published earlier. The unscented Kalman filter (UKF) is used to optimize model input parameters to fit measured data pre-intervention. After optimization, the valve treatment is simulated by significantly r

SUBMITTER: Meiburg R 

PROVIDER: S-EPMC7583387 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature