Influence of flow pattern on the parameter estimates of a simple breathing mechanics model

The first-order model of breathing mechanics is widely used in clinical practice to assess the viscoelastic properties of the respiratory system. Although simple, this model takes the predominant features of the pressure-flow relationship into account but gives highly systematic residuals between measured and model-predicted variables. To achieve a better fit of the entire data set, an approach hypothesizing deterministic time-variations of model parameters, summarized by information-weighted histograms was recently proposed by Bates and Lauzon. The present study uses flow and pressure data measured in intensive care patients to evaluate the real potential of this approach in clinical practice. Information-weighted histograms of the model parameters, estimated by an on-line identification algorithm, were first constructed by taking into account the parameter percentage standard deviations. Then, the influence of the respiratory flow pattern on the calculated histograms was evaluated by the Kolmogorov-Smirnov statistical test. The results show that the method gives good reproducibility under stable experimental conditions. In addition, for a given airflow waveform, an increase in respiratory frequency shifts the histograms representing time-varying viscous properties strongly versus lower values, whereas it shifts the histograms representing time-varying elastic properties slightly versus higher values. On the other hand, the same histograms were highly dependent on the airflow waveform, especially for the viscous properties. Even in a limited experimental work, in all the conditions considered, the method provides results which agree well with the physiological knowledge of nonlinear and multicompartment behavior of respiratory mechanics.