A Family of Physiological Models to Simulate Human Gas Exchange

Kretschmer, Jörn; Riedlinger, Axel; Becher, Tobias; Schädler, Dirk; Weiler, Norbert; Möller, Knut

doi:10.1515/bmt-2013-4353

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All those patients (8, 11, 14, and 16) comprised a high V /Q-mismatch, a characteristic the one-compartment model can simply not reproduce. Such might be addressed in models of higher complexity, which leads to our proposal of using multiple model versions of different complexity allowing adaption of model simulation to the actual disease state of the patient [14]. However, the model was able to reproduce all real patient data sets with a mean deviation below 10%; again with best results at 60% FiO 2 .…”

Section: Discussionmentioning

confidence: 99%

Determining the Optimal Calibration Point for the Clinical Application of a Simple Model of Human Gas Exchange

Kretschmer

Riedlinger²,

Möller³

2014

Computers and Advanced Technology in Education / 820: Modelling, Simulation and Identification / 821: Environmental Management

View full text Add to dashboard Cite

Mathematical models of the human physiology allow both the identification of hidden patient parameters and the prediction of patient reactions towards changes in the therapy regime. The knowledge of hidden patient parameters allows a doctor to further evaluate a patient's stage of illness, while the prediction of patient reactions allows optimization of therapy settings to reach a desired therapy goal. Such physiological modeling might be applied to mechanically ventilated patients in order to optimize the applied ventilator settings. Mechanical ventilation is a life-saving routine therapy; however it bears the risk of further injuries if ventilator settings are not set properly. We have previously presented a simple model of human gas exchange that is able to predict the inspired oxygen fraction (FiO 2) to reach a desired level of arterial oxygen partial pressure (PaO 2) in a patient. The model is calibrated using one blood gas measurement, thus measurement noise may strongly influence predictive quality. We therefore applied artificial and real patient data to determine the optimal calibration point where influence of measurement noise is minimal and prediction quality is optimal. Results show, that prediction quality strongly increases when calibration is done at 60% to 70% FiO 2 or 150mmHg to 200mmHg PaO 2 , respectively.

show abstract

Section: Discussionmentioning

confidence: 99%