Evaluation of an algorithm to choose between competing models of respiratory mechanics

Abstract

Model based decision support helps in optimizing therapy settings for individual patients while providing additional insight into a patient’s disease state through the identified model parameters. Using multiple models with different simulation focus and complexity allows adapting decision support to the current clinical situation and the available data. A previously presented set of numerical criteria allows selecting the best model based on fit quality, model complexity, and how well the parameter values are defined by the presented data. To systematically evaluate those criteria in an algorithm we have created insilico data sets using four different respiratory mechanics models with three different parameter settings each. Each of those artificial patients was ventilated with three different manoeuvres and the resulting data was used to identify the same models used to create the data. The selection algorithm was then presented with the results to select the best model. Not considering determinateness of the identified model parameters, the algorithm chose the same model that was used to create the data in 78%, a more complex model in 5% and a less complex model in 18% of all cases. When including the determinateness of model parameters in the decision process, the algorithm chose the same model in 42% of the cases and a less complex model in 56% of all cases. In 2% of the presented cases, no model complied with the required criteria.

Evaluating the Combined Optimization of Oxygenation and Ventilation in a Patient Simulator

“…This hierarchy can be exploited for parameter identification when the parameter values of the models of lower order are used as a basis for selecting appropriate initial guesses for the identification of the more complex models [5]. To select the model that fits the given data best, an algorithm has been introduced previously that selects the best model based on fit quality and the number of parameters in the models [6]. The models available for the presented optimization approach are a model of first order (FOM) [5], a viscoelastic model (VEM) [5], a recruitment model (PRM) [7] and a recruitment model with viscoelastic elements (PRVEM) [8].…”