Book contents
- Frontmatter
- Contents
- Preface
- Notation
- 1 Introduction
- 2 Collecting data
- 3 The linear single-compartment model
- 4 Resistance and elastance
- 5 Nonlinear single-compartment models
- 6 Flow limitation
- 7 Linear two-compartment models
- 8 The general linear model
- 9 Inverse models of lung impedance
- 10 Constant phase model of impedance
- 11 Nonlinear dynamic models
- 12 Epilogue
- References
- Index
5 - Nonlinear single-compartment models
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- Notation
- 1 Introduction
- 2 Collecting data
- 3 The linear single-compartment model
- 4 Resistance and elastance
- 5 Nonlinear single-compartment models
- 6 Flow limitation
- 7 Linear two-compartment models
- 8 The general linear model
- 9 Inverse models of lung impedance
- 10 Constant phase model of impedance
- 11 Nonlinear dynamic models
- 12 Epilogue
- References
- Index
Summary
The linear single-compartment model shown in Fig. 3.1 accurately mimics the mechanical behavior of a healthy lung during resting breathing or mechanical ventilation. This is rather remarkable given the lung's enormous structural complexity, and shows that its thousands of airways and millions of alveoli normally act together in a highly coordinated fashion. This ceases to be the case, however, when the lung is forced to operate at volumes or frequencies outside the range of normal breathing, or when pathologies set in. Such conditions degrade the fitting performance of the linear single-compartment model, producing elevated values of SSR (Eq. 3.11) and decreased values of CD (Eq. 3.31). Contrast, for example, the two model fits shown in Fig. 5.1. The data from Patient A shown in the upper panel are the same as shown in Fig. 3.6B, and the fit provided by Eq. 3.5 is excellent with CD = 0.995. The data from Patient B in the lower panel are also the ensemble average of 60 s of regular mechanical ventilation, and the fit is still very good with CD = 0.944. Nevertheless, there are sizeable deviations between the data and the model fit for patient B, suggesting that something was going on in the lungs of this patient that is not represented in the linear single-compartment model.
- Type
- Chapter
- Information
- Lung MechanicsAn Inverse Modeling Approach, pp. 82 - 96Publisher: Cambridge University PressPrint publication year: 2009