Uncertainty in model‐based treatment decision support: applied to aortic valve stenosis
| Autor: | Frans N. van de Vosse, Roel Meiburg, Marcel C. M. Rutten, Wouter Huberts |
|---|---|
| Přispěvatelé: | Cardiovascular Biomechanics, Eindhoven MedTech Innovation Center, EAISI Health, Biomedische Technologie, RS: Carim - H07 Cardiovascular System Dynamics |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Monte Carlo Markov chain
Computer science medicine.medical_treatment FLOW 02 engineering and technology 030204 cardiovascular system & hematology 0302 clinical medicine Valve replacement patient specific Polynomial chaos Estimation theory Applied Mathematics Uncertainty STATE Markov Chains prediction uncertainty Treatment Outcome Computational Theory and Mathematics Modeling and Simulation Aortic valve stenosis Aortic Valve symbols parameter estimation PARAMETER-ESTIMATION 0206 medical engineering INDEXES Biomedical Engineering PRESSURE unscented Kalman filter 03 medical and health sciences symbols.namesake DATA ASSIMILATION Control theory medicine Research Article ‐ Applications Humans Computer Simulation Molecular Biology Research Article ‐ Application HYPERTENSION GLOBAL SENSITIVITY-ANALYSIS aortic stenosis Markov chain Monte Carlo Kalman filter Aortic Valve Stenosis medicine.disease 020601 biomedical engineering Confidence interval Test case IMPLANTATION Software |
| Zdroj: | International Journal for Numerical Methods in Biomedical Engineering, 36(10):e3388. Wiley-Blackwell International Journal for Numerical Methods in Biomedical Engineering International Journal for Numerical Methods in Biomedical Engineering, 36(10):3388. Wiley-Blackwell |
| ISSN: | 2040-7939 |
| Popis: | 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. 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 stenosisWe showed a data assimilation approach (Unscented Kalman Filter) is suitable for parameter optimization for this model based on available clinical data, with estimated optimization uncertaintyWe expanded parameter optimization via Unscented Kalman Filtering via sensitivity‐based weightingWe coupled uncertain parameter optimization with a polynomial chaos expansion approach to propagate uncertain parameters in treatment prediction |
| Databáze: | OpenAIRE |
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