Practical identifiability analysis of a minimal cardiovascular system model.
OBJECTIVE
Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume.


METHODS
An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter.


RESULTS
Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation.


CONCLUSIONS
This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable.
GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium. Electronic address: a.pironet@alumni.ulg.ac.be.
Antoine
A
Pironet
Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
Paul D
PD
Docherty
GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium.
Pierre C
PC
Dauby
Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
J Geoffrey
JG
Chase
GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium.
Thomas
T
Desaive
D000465:Algorithms; D008955:Models, Cardiovascular; D008991:Monitoring, Physiologic / Q000379:methods*; D013318:Stroke Volume / Q000502:physiology; D014690:Venous Pressure / Q000502:physiology
Cardiovascular system; Mathematical model; Parameter identification; Practical identifiability