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

Abstract

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.

Estimating respiratory mechanics in the presence of flow limitation

“…The RLS algorithm was initialized with  0 ϭ 0 and Q 0 ϭ 10 6 I (I is the identity matrix). For both simulated and experimental data, mem was 0.4 s, similar to previous studies (1,3). Figure 2 shows an example of a complete breath of V and P simulated by the model, together with the recursively estimated R and E both without IFL ( Fig.…”

“…Bates and Lauzon (3) found, however, that those portions of the data that produced such meaningless values invariably contained very little information, as reflected in the corresponding diagonal values of the information matrix (these diagonal values are proportional to the estimated variances of the estimated parameters). This led to the notion of the information-weighted histogram proposed by Bates and Lauzon (3) and used subse-quently by Avanzolini et al (1). Here, the variations in R and E are represented in a histogram, but the contribution of each value to the histogram is weighted by the inverse of the corresponding diagonal element of the information matrix.…”

Análise comparativa da complacência do sistema respiratório em três diferentes posições no leito (lateral, sentada e dorsal) em pacientes submetidos à ventilação mecânica invasiva prolongada

“…This was related to compliance of the respiratory system, which is a measure of elasticity and resistance to deformation when facing any force represented by varied degrees of effort. 15,16 It was also seen that the pulmonary flow is lesser in dorsal decubitus than in the lateral and sitting position (p=0.044). This is probably due to a greater mechanical compression in the chest when in dorsal decubitus, thereby reducing respiratory flow.…”

Dual Unscented Kalman Filter and Its Applications to Respiratory System Modelling

“…As the model has more degree of freedom (incorporates more parameter (Avanzolini et al, 1995) or incorporates the nonlinearity (Athanasiades et al, 2000)) the uncertainties approaches to the statistical noise that is usually Gaussian distributed. Here, we started with the assumption that the residuals are the white Gaussian noises and estimation and measured noisy time series can be fit to the respiratory models by the estimation methods.…”

“…Elemental equations define the relationship between appropriate pressures and volumetric flow at the specific regions of respiratory system whereas the system state and measurement equations are the mathematical descriptions of the whole system behaviour. Theoretical and experimental studies reveal that respiratory system models may be linear and nonlinear in both state and parameters depending on the system identification technique, considered disease conditions and experimental methodology (Polak & Mroczka, 2006); (Bates & Lutchen, 2005); (Avanzolini et al, 1995). For the interested reader, discussion and comparisons on the linear and nonlinear models can be found in the literature (Diong et al, 2007); (Yuan et al, 1998).…”