Abstract
Inhomogeneity in the lung impairs gas exchange and can be an early marker of lung disease. We hypothesized that highly precise measurements of gas exchange contain sufficient information to quantify many aspects of the inhomogeneity noninvasively. Our aim was to explore whether one parameterization of lung inhomogeneity could both fit such data and provide reliable parameter estimates. A mathematical model of gas exchange in an inhomogeneous lung was developed, containing inhomogeneity parameters for compliance, vascular conductance, and dead space, all relative to lung volume. Inputs were respiratory flow, cardiac output, and the inspiratory and pulmonary arterial gas compositions. Outputs were expiratory and pulmonary venous gas compositions. All values were specified every 10 ms. Some parameters were set to physiologically plausible values. To estimate the remaining unknown parameters and inputs, the model was embedded within a nonlinear estimation routine to minimize the deviations between model and data for CO2, O2, and N2 flows during expiration. Three groups, each of six individuals, were studied: young (20-30 yr); old (70-80 yr); and patients with mild to moderate chronic obstructive pulmonary disease (COPD). Each participant undertook a 15-min measurement protocol six times. For all parameters reflecting inhomogeneity, highly significant differences were found between the three participant groups ( P < 0.001, ANOVA). Intraclass correlation coefficients were 0.96, 0.99, and 0.94 for the parameters reflecting inhomogeneity in deadspace, compliance, and vascular conductance, respectively. We conclude that, for the particular participants selected, highly repeatable estimates for parameters reflecting inhomogeneity could be obtained from noninvasive measurements of respiratory gas exchange. NEW & NOTEWORTHY This study describes a new method, based on highly precise measures of gas exchange, that quantifies three distributions that are intrinsic to the lung. These distributions represent three fundamentally different types of inhomogeneity that together give rise to ventilation-perfusion mismatch and result in impaired gas exchange. The measurement technique has potentially broad clinical applicability because it is simple for both patient and operator, it does not involve ionizing radiation, and it is completely noninvasive.
Highlights
Inhomogeneities in gas exchange within the lung occur physiologically through, for example, small-scale variations in specific ventilation of lung units, the effects of gravity on lung expansion, and the effects of different dead space-to-alveolar volume ratios between hilar and peripheral lung units
Synthetic data were generated “noise free”, and with two different noise models to reflect the measurement error associated with standard equipment [24] and the measurement error associated with the molecular flow sensing device (MFS) device used in this study [7]
Four different types of “noise” were added to the signals: random sensor error was simulated with a Gaussian measurement error incorporated into the flow signal; the finite response time of the gas analyzers was reflected by incorporating first-order dynamics into the measured gas fractions; temporal misalignment of the flow and gas fractions was simulated by shifting the flow and gas fraction time series relative to each other; and errors in recorded volumes were simulated by adding a Gaussian random walk into the time series for flow at the MFS
Summary
Inhomogeneities in gas exchange within the lung occur physiologically through, for example, small-scale variations in specific ventilation of lung units, the effects of gravity on lung expansion, and the effects of different dead space-to-alveolar volume ratios between hilar and peripheral lung units. Because pathological change within the lung tends not to affect all lung units [13], changes in the degree of inhomogeneity in the lung may provide a sensitive early indicator of disease. The approach we adopt is to devise a model of gas exchange in the lung that represents different aspects of inhomogeneity through a set of parameter values. This model is nested within a nonlinear leastsquares estimation routine to determine the model parameters that best approximate the manner in which CO2, O2, and N2 emerge during expiration over the course of the experimental period
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