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 reducing valve resistance. Uncertain model parameters are then propagated using a polynomial chaos expansion approach. To test the proposed framework, three in silico test cases are developed with clinically feasible measurements. Quality and availability of simulated measured patient data are decreased in each case. The UKF approach is compared to a Monte Carlo Markov Chain (MCMC) approach, a well-known approach in modelling predictions with uncertainty. Both methods show increased confidence intervals as measurement quality decreases. By considering three in silico test-cases we were able to show that the proposed framework is able to incorporate optimization uncertainty in model predictions and is faster and the MCMC approach, although it is more sensitive to noise in flow measurements. To conclude, this work shows that the proposed framework is ready to be applied to real patient data. Novelty Statement • We developed a lumped parameter model to predict changes in haemodynamic parameters after valve replacement therapy in aortic stenosis

Monitoring variability in parameter estimates for lumped parameter models of the systemic circulation using longitudinal hemodynamic measurements

“…Previous research has shown promising results for personalizing cardiovascular models [5][6][7][8][9][10][11][12] or predicting intervention effects in settings of localized cardiovascular disease and critical care [13,14]. In this work, we investigated the application of such models as a tool for monitoring of the left ventricle and systemic circulation in apparently healthy adults at risk of developing cardiovascular disease.…”

Surrogate‐based Bayesian calibration of biomechanical models with isotropic material behavior

“…Bayesian calibration was considered in Reference 13 to infer boundary conditions and the associated uncertainties in a virtual surgery prediction model and in Reference 14 for lumped parameter models of cardiovascular physiology. In Reference 15, calibration and propagation of uncertainties, where the latter was based on PC expansions, was discussed in a transaortic valve implantation context. In Reference 16, the authors carried out a sensitivity analysis in a preparation step to restrict the calibration procedure to the identifiable parameters of a model for the main pulmonary artery.…”