Hierarchical structure of human gas exchange models to improve parameter identification

Mathematical models can be used to simulate a patient's respiratory system and thus predict the outcome of a change in therapy settings. Therefore, mathematical models might be exploited to support therapeutic decision making in mechanical ventilation. In interaction with models of respiratory mechanics and cardiovascular dynamics, gas exchange models may help to find optimal ventilator settings achieving sufficient oxygenation of the patient along with avoiding further lung damage due to high peak inspiratory pressures. Considering the risk of oxygen toxicity, gas exchange models may calculate optimal inspired oxygen fraction (F_{i, 02}) to achieve a desired goal for blood gas oxygenation (P_{a, 02}). In general, physiological models should be kept as simple as possible to reduce the number of model parameters that have to be identified for adaptation to the individual patient. However, in case of severe lung disease simple gas exchange models often are not able to simulate patient physiology adequately. More complex models are necessary and identification may be intractable due to a higher number of model parameters. Thus, depending on the complexity of the employed model a different amount of information is required for identification. A hierarchical structure of different gas exchange models is presented that might be exploited to simplify parameter identification of complex models. A stepwise identification of the model parameters may lead to an accelerated and robust identification process as shown in a retrospective analysis of patient data.