This study shows how sensitivity analysis and subset selection can be employed
in a cardiovascular model to estimate total systemic resistance,
cerebrovascular resistance, arterial compliance, and time for peak systolic
ventricular pressure for healthy young and elderly subjects. These quantities
are parameters in a simple lumped parameter model that predicts pressure and
flow in the systemic circulation. The model is combined with experimental
measurements of blood flow velocity from the middle cerebral artery and
arterial finger blood pressure. To estimate the model parameters we use
nonlinear optimization combined with sensitivity analysis and subset selection.
Sensitivity analysis allows us to rank model parameters from the most to the
least sensitive with respect to the output states (cerebral blood flow
velocity and arterial blood pressure). Subset selection allows us to identify
a set of independent candidate parameters that can be estimated given limited
data. Analyses of output from both methods allow us to identify five
independent sensitive parameters that can be estimated given the data.
Results show that with the advance of age total systemic and cerebral
resistances increase, that time for peak systolic ventricular pressure is
increases, and that arterial compliance is reduced. Thus, the method discussed
in this study provides a new methodology to extract clinical markers that
cannot easily be assessed noninvasively.
Mathematics Subject Classification: Primary: 92C30, 92C35, 92C50, 65L09; Secondary: 93B40.