Antoine Pironet1, Paul D Docherty2, Pierre C Dauby3, J Geoffrey Chase2, Thomas Desaive3. 1. 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. 2. Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand. 3. GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium.
Abstract
BACKGROUND AND 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.
BACKGROUND AND 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.
Authors: Erin J Mansell; Signe Schmidt; Paul D Docherty; Kirsten Nørgaard; John B Jørgensen; Henrik Madsen Journal: J Pharmacokinet Pharmacodyn Date: 2017-08-22 Impact factor: 2.745
Authors: Simon Habran; Thomas Desaive; Philippe Morimont; Bernard Lambermont; Pierre C Dauby Journal: Comput Math Methods Med Date: 2020-04-06 Impact factor: 2.238