Uncertainty in model‐based treatment decision support: Applied to aortic valve stenosis. (5th August 2020)

• Record Type: Journal Article
• Authors: Meiburg, Roel; Huberts, Wouter; Rutten, Marcel C. M.; van de Vosse, Frans N.
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 36:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-05
• Subjects: aortic stenosis -- Monte Carlo Markov chain -- parameter estimation -- patient specific -- prediction uncertainty -- unscented Kalman filter; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3388
• Languages: English
• ISSNs: 2040-7939

Abstract

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. Abstract : In this study, we aim to provide a framework to predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis. The framework involves uncertain input parameter optimization using an unscented Kalman filter approach. Valve replacement treatment is then simulated, and uncertainties are propagated via polynomial chaos expansions. The framework is able to capture measurement quality in prediction confidence intervals and is ready to be applied to patient data. Novelty Statement We developed a lumped parameter model to predict changes in haemodynamic parameters after valve replacement therapy in aortic stenosis We showed a data assimilation approach (Unscented Kalman Filter) is suitable for parameter optimization for this model based on available clinical data, with estimated optimization uncertainty We expanded parameter optimization via Unscented Kalman Filtering via sensitivity‐based weighting We coupled uncertain parameter optimization with a polynomial chaos expansion approach to propagate uncertain parameters in treatment prediction