| dc.description.abstract | The major challenge for applying cardiovascular models in personalized medicine
is as follows: How certain are the predictions of a cardiovascular model, taking
the natural variations and measurement uncertainty of model inputs into account?
To address this challenge, we combined methodologies for uncertainty quantification
(UQ) and sensitivity analysis (SA) with cardiovascular models. In a comprehensive
guide to UQ and SA, we showed how to quantify the uncertainties of
model predictions and the sensitivity of model predictions with respect to model
inputs. The approach is exemplified for two clinically relevant models, predicting
the following: i.) the severity of coronary artery stenosis, which is a predisposition
to stroke and transient ischaemic attack, and ii.) the total arterial compliance,
which is considered to be a cardiovascular risk factor, indicating the development
of hypertension and atherosclerosis. Furthermore, we developed a framework for
UQ and SA in a one-dimensional blood flow model, which can be applied for
patient-specific simulations of the cardiovascular system under healthy and diseased
conditions. Using this framework, we identified that the aortic arteries play
a key role in the development of age-related hypertension. Moreover, we demonstrated
how UQ and SA can be applied to guide the selection of the most suitable
models, exemplified with the choice of arterial wall models when simulating onedimensional
arterial networks.
In the future, personalized computer models will be applied in medical practice
and become an integral part of cellphone apps for diagnostics, robots for interventions,
and clinical software for diagnostics and intervention planning. Consequently,
vital decisions will increasingly be based on the predictions of computer
models. With this outlook, the reliability of the applied computer models need to
be assessed and proven using methodologies such as those presented in this thesis. | nb_NO |
