Potential for noninvasive assessment of lung inhomogeneity using highly precise, highly time-resolved measurements of gas exchange

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.

Keywords: dead space; lung compliance; lung vascular conductance; respiratory function tests; ventilation-perfusion ratio.

Figures

Fig. 1.

Model of lung inhomogeneity. A: schematic for a single lung unit (index i) of the model. ΔVtot is change in total lung volume from functional residual capacity; Q̇tot is the total pulmonary flow; other symbols are as defined in Table 1. B: illustration of the continuous bivariate log normal distribution for the fractional lung compliance–fractional alveolar volume ratio (ln[FCL(x,y):FVA(x,y)], or ln(CL*) where CL* is the standardized lung compliance) and fractional lung vascular conductance–fractional alveolar volume ratio (ln[FCd(x,y):FVA(x,y)], or ln(Cd*) where Cd* is the standardized lung vascular conductance).

Fig. 2.

Example record of a lung inhomogeneity test conducted using in-airway molecular flow sensing. Shown are the tidal flows for oxygen, carbon dioxide, and nitrogen. The upward trend for oxygen indicates oxygen consumption, and the downward trend for carbon dioxide indicates carbon dioxide production. The absence of any trend in the nitrogen record reflects the lack of any net uptake or production of nitrogen apart from the washout period which begins at ∼10 min, when the inspirate is changed from air to 100% oxygen. Although not visible, data are recorded every 10 ms.

Fig. 3.

Illustration of the fit of the lung inhomogeneity model to the data for one participant. A: mixed expired nitrogen values (one per breath) over the entire course of the inhomogeneity measurement. The quality of the fit is such that the data essentially overlap the model output throughout the entire nitrogen washout phase. B–D: fit of the model to the data for single breaths (expirations) sampled from the air-breathing phase (B), near the start of the nitrogen washout phase (C), and midway through the nitrogen washout phase (D). In each plot, measured data are shown with an unbroken line, and simulated data are shown with a broken line. The model output closely reproduces the form of the expiratory profile in each case.

Fig. 4.

Example records for the first section of the nitrogen washout phase of the experimental protocol. A–C: end-tidal N2 fractions (FET,N2) plotted against lung turnover. D–F: the normalized slope of the N2 record during phase III of expiration. A, D: young healthy participant. B, E: old participant. C, F: participant with COPD.

Fig. 5.

Example distributions recovered by fitting the lung inhomogeneity model to the data. A, C, and E: distributions recovered from healthy participant. B, D, and F: distributions recovered from a participant with COPD. A and B: distributions for the fractional lung compliance to fractional alveolar volume ratio (FCL:FVA); the volume distribution is identically that for CL*. C and D: distributions for fractional vascular conductance to fractional alveolar volume ratio (FCd:FVA); the volume distribution is identically that for Cd*. E and F: distributions for alveolar ventilation to perfusion (V̇A:Q̇), as calculated from the model parameters. In all cases, the distributions are wider for the participant with COPD compared with the healthy young participant. The distributions have been illustrated in a discretized form, with 50 points evenly spaced on a logarithmic scale from 0.005 to 100.

Fig. 6.

Example distribution for the dead space-to-alveolar volume ratio, (FVDVD,tot):(FVAVA,tot). A: healthy young participant. B: participant with COPD. The volume distributions shown are identically those for (VD,tot/VA,tot)VD*, where VD* is the standardized dead space. Note that the mean value for this distribution in the participant with COPD is around double that of the healthy young participant and that the distribution is substantially wider. The distributions have been illustrated in a discretized form, with 50 points evenly spaced on a scale from 0 to 0.25.

Fig. 7.

Distributions associated with the mean lung parameters from the 3 groups of participants. A–C: contour plots for the bivariate log normal distributions for the young group (A), the old group (B), and the COPD group (C). Contour intervals are values for the probability density function. D: normal distributions for the dead space relative to alveolar volume at FRC for all 3 groups. These distributions have been illustrated in a discretized form, with 100 points evenly spaced on a scale from 0 to 0.4.