| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Departments of 1Biomedical Engineering and 2Chemical Engineering and Materials Science, University of California, Irvine, Irvine, California
Submitted 4 January 2005 ; accepted in final form 1 October 2005
 |
ABSTRACT |
|---|
 TOP ABSTRACT METHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
gas exchange; trumpet model
Several techniques have been developed to partition FENO into airway and alveolar contributions (6, 7, 10, 18, 27, 28, 30) to provide information on region-specific pathobiology (8, 11–13, 17, 24–27). These methods include both vital capacity maneuvers and tidal breathing and generally alter exhalation flow to sample different gas-phase residence times in the airways. By doing so, the alveolar and airway regions can be characterized by flow-independent parameters which include the steady-state alveolar concentration (CANO), the airway wall diffusing capacity (DawNO), and either the airway wall concentration (CawNO) or the maximum airway wall flux (JawNO, equal to the product DawNO·CawNO) (6, 7).
Early models of NO exchange neglected axial diffusion of NO in the gas phase, as well as the increasing cross-sectional area of the airway tree with increasing airway generation (i.e., trumpet shape). These simplifications generated errors in the estimation of CawNO and JawNO' (21–23, 32). In addition, the variance of DawNO was larger than other parameters, and the accuracy depends on the residence time of the air in the airway compartment (25). A unique challenge in determining DawNO is the need to sample very low (<50 ml/s) exhalation flows (29). These very low exhalation flows can be difficult to perform, especially for young subjects and people with compromised lung function. Accurate estimation of DawNO is particularly interesting because initial studies suggest that it is elevated in asthma but may be independent of steroid use (24, 27), unlike FENO and CawNO.
Recently, we have developed a new technique that focuses on the determination of the airway wall NO parameters (CawNO, DawNO, and JawNO') (21). The technique uses a series of different breath-holding times that significantly improves the accuracy of determining DawNO and suggests that, indeed, the estimation of DawNO also depends on axial diffusion and airway geometry.
The goal of the present study is to alter the rate of axial molecular diffusion of NO in the gas phase of the airway tree by using heliox (80% helium, 20% oxygen) as the insufflating gas and then accurately estimate airway wall NO parameters using our recently described breath-holding technique. Because the airway NO parameters characterize features of the airway wall or tissue, such as airway wall surface area, tissue thickness, and net rate of tissue production (28), they should be independent of the physical properties of the insufflating gas. To investigate this premise, our model of NO exchange must capture the relevant physical properties of the insufflating gas (e.g., rate of molecular diffusion), including the space it occupies (e.g., airway geometry).
|
METHODS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
Subjects. Nine healthy adults (age 21–38 yr, five female) participated in the study (Table 1). All subjects had a ratio of forced expiratory volume in 1 s to forced vital capacity (FEV1/FVC) of >0.75 at the time of testing. In addition, all subjects had no history of smoking at any time and no history of cardiovascular, pulmonary, or neurological diseases. The Institutional Review Board at the University of California, Irvine approved the protocol, and written, informed consent was obtained from all subjects.
|
2 s. A positive pressure >5 cmH2O was maintained during the breath hold and exhalation to prevent nasal contamination (2). A schematic of the experimental apparatus has been previously presented (29). After indexes of NO exchange dynamics were measured, general spirometry including FVC and FEV1 were measured in all subjects (Vmax229; Sensormedics, Yorba Linda, CA) by using the best performance (Table 1) from three consecutive maneuvers.
Airstream analysis.
A chemiluminescence NO analyzer (NOA280, Sievers, Boulder, CO) was used to measure the exhaled NO concentration. The instrument was calibrated on a daily basis using a certified NO gas (45 ppm NO in 100% N2 for air calibration and 45 ppm NO in 100% He for heliox calibration, Sievers). The zero-point calibration was performed with an NO filter (Sievers) immediately before the collection of a profile. Calibration with 
80% of carrier gas (either nitrogen or helium as in the case of air or heliox, respectively) balanced with oxygen resulted in a negligible change in the response of the instrument (<2% for helium). The flow rate and pressure signals were measured by using a pneumotachometer (RSS100HR, Hans Rudolph, Kansas City, MO). The pneumotachometer was calibrated before each subject and set to provide the flow in units of STPD and pressure in units of cmH2O. The software of the pneumotachometer accounts for changes in gas properties (e.g., viscosity) when using heliox as the insufflating gas.
Empirical data analysis. Experimental exhalation profiles after breath hold from air and heliox breathing were characterized empirically (independent of a mathematical model or "model independent") by the peak or maximum observed concentration in phase I and II (Fig. 2A) of the exhalation profile, CNO peak; the width of phase I and II, W50, defined as the exhaled volume in which the NO concentration was greater than 50% of CNO peak; VI,II, the total exhaled volume of phase I and II; and AI,II, the total mass or volume of NO (area under the curve) in phase I and II.
|
Trumpet model.
Mathematical models to estimate airway wall NO parameters were developed for two cases: 1) trumpet airway in the absence of axial diffusion (T), and 2) trumpet airway in the presence of axial diffusion (T-AD). Details of these models, including the derivation of the governing equations and solutions, have been previously presented (21), and only the salient features will be described. For each subject, airway geometry was characterized by appropriately scaling the lengths and diameters of Weibel's data of the human airway tree (4, 33), on the basis of the conducting airway volume (Vaw) of generations 0-17 of each subject (4, 33). The trumpet shape of the airway (15, 20, 23) is shown in Fig. 1, and was captured using the following relationship between airway cross-sectional area (Ac) and axial position, z (21):
 | (1) |
|
Statistics. Data were analyzed by one-way and two-way repeated-measures ANOVA, followed by paired or unpaired t-tests as appropriate, if the ANOVA analysis demonstrated statistical significance (P < 0.05). All variables were assumed to be normally distributed, and all statistical tests were performed on raw data scores. Outliers were defined by raw values that exceed three standard deviations from the mean. A P value < 0.05 was considered statistically significant.
|
RESULTS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
CNO peak, W50, VI,II, and AI,II for all nine subjects are presented in Fig. 3 to demonstrate model-independent differences in the exhaled NO profile as a function of breath-hold time as well as the differences between air and heliox breathing. For both air and heliox breathing, CNO peak (Fig. 3A) and AI,II (Fig. 3D) were both strong positive functions of breath-hold times for all nine subjects. However, breath-hold time did not impact W50 or VI,II (Fig. 3, B and C). CNO peak did not depend on the presence of heliox; however, W50 and AI,II (except 5-s breath hold) were all significantly reduced in the presence of heliox, independent of breath-hold times. VI,II tended to be lower in the presence of heliox but only reached statistical significance for breath-hold times of 15 and 20 s.
|
|
75%. For heliox, CawNO and JawNO' were also significantly (P < 0.001 for CawNO and P < 0.001 for JawNO') increased by more than 22-fold and 4-fold, respectively; DawNO was (P = 0.005) decreased by 80%. Of note is the observation that determined airway wall parameters are independent of the insufflating gas when axial diffusion is included in the model (i.e., T-AD). NO concentration as a function of airway volumetric position is shown in Fig. 5 for the T-AD model for both air and heliox breathing when breath-hold time was set to 30 s. Recall, for both cases, that total mass of NO within the airway tree is not different because this is the experimental variable for which the determined model parameters are chosen to match. Because estimated NO parameters do not depend on the insufflating gas for model T-AD, the mean parameter set determined from air (1,439 ppb for CawNO and 3.70 pl·s–1·ppb–1 for DawNO) was used to generate the NO profile within the airway tree. The presence of heliox reduces the concentration of NO along the airway tree during breath hold but does not impact CNO peak.
|
|
|
DISCUSSION |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
Impact of Insufflating Gas
When heliox was used as the insufflating gas in the present study, exhaled NO concentration and thus the total mass of NO exhaled (AI,II) was decreased (Figs. 2 and 3). The reduced AI,II is due primarily to the reduced W50 (thinner peak causes a small area) because CNO peak is independent of the insufflating gas (Fig. 2). Molecular diffusion of NO in helium is enhanced relative to nitrogen. Thus, in the presence of heliox, the rate of molecular diffusion of NO is increased 2.3-fold. The reduced W50 represents depletion of NO in the smaller airways due to enhanced axial diffusion of NO from the airways to the alveolar region. CNO peak occurs in the first part of the exhaled breath, which is far away from the sink (i.e., the alveolar region) and is therefore not impacted by altering the rate of axial diffusion. This result is consistent with the concentration profile of NO in the airway tree as predicted by the trumpet model (Fig. 5) as well as previous reports (22, 23).
VI,II represents the volume of phase I and II of the exhaled profile as defined by the point of zero slope in the exhalation profile (Fig. 2A) and is therefore an estimate of that point in the exhalation when all airway gas has been expired by convection, including that in the respiratory transition region (i.e., generations 18-23). VI,II was statistically reduced in the presence of heliox for two of the five breath-hold times (Fig. 3C), and thus this trend may also explain the reduced AI,II observed for heliox. The trumpet model describes an abrupt transition between the airway and alveolar region, and this boundary is at a fixed concentration of CANO = 0. Thus the trumpet model structure, including the boundary conditions, dictates that a zero slope occurs at an exhaled volume equal to the volume of the trumpet (see Fig. 5) and that this volume would be independent of the insufflating gas. However, it is also evident from Fig. 5 that the slope of the concentration profile becomes flatter at smaller volumes in the presence of heliox because of enhanced loss of NO to the alveolar region. This observation, combined with normal experimental noise and the fact that an abrupt transition between the airways and the alveolar region does not occur, may account for the experimental observations.
As shown in Fig. 4, airway wall NO parameters depend strongly on the insufflating gas when axial diffusion is neglected in the model (model T). In contrast, when axial diffusion is included (model T-AD), the airway wall NO parameters are independent of the insufflating gas. The airway wall NO parameters describe the airway wall tissue and depend on such characteristics as wall surface area, tissue thickness, and net rate of tissue production (28). Thus determined values for the airway wall NO parameters should be independent of the properties of the gas phase, such as the molecular diffusion coefficient. Indeed, we have previously demonstrated theoretically that the rate of radial diffusion of NO from the airway wall is independent of the gas phase (28). Our result suggests that a model of the airways that considers both trumpet geometry and axial diffusion captures the essential features of airway NO exchange.
Validity of the Model Assumptions and Structure
We assume that as z approaches zero, exhaled NO concentration in the gas phase of the airways (CNO) approaches the steady-state alveolar concentration, CANO (Fig. 1). Although CANO has been shown by many investigators to be nonzero, the values are generally <2 ppb, much lower than those observed in the airway tree during the breath hold. Thus, for simplicity, CANO is set to zero as one of the boundary conditions. Van Muylem et al. (32) also explored the impact of axial diffusion of NO exchange by accounting for the trumpet shape of the airway tree. In their simulation, a zero CANO had minimal impact on the estimated steady-state NO concentration at 50 ml/s exhalation flow compared with CANO = 1.8 ppb (28.7 ppb vs. 29.8 ppb, respectively). Thus setting CANO to zero in the present simulation should have a minimal impact on the airway wall NO parameters.
The molecular diffusivity of NO (0.52 cm2/s in heliox and 0.23 cm2/s in air) is an approximate value (19) and is assumed to be maintained in the airway trumpet (up to generation 17) during the breath hold. The molecular diffusivity of NO in the alveolar space is likely to be somewhere between these two values as the inspired heliox mixes with air in the residual volume (34). However, the fact that a pre-breath-hold tidal breathing wash-in period of heliox for 2 min has been shown not to impact exhaled NO concentrations (22) suggests that the rate-limiting location of axial diffusion for NO during the breath hold is not in the residual volume and alveolar space, but rather exists in small airways.
Accumulation of NO in the airway space during filling and evacuation of the airway tree before and after the breath hold may introduce error, which our model and parameter estimation algorithm do not consider. However, the subjects were instructed to inspire rapidly, generally over the course of <3 s, and thus maintained an average inspiration flow of >1 l/s. Thus filling of the airway tree at the end of inspiration would generally take <0.2 s and could be considered negligible. The exhalation flow was recorded and was >200 ml/s [e.g., experimental average flow rate (SD) after the 20-s breath hold of air was 229 ml/s (SD 44)] to ensure evacuation of the airway space in 2 s. This delay may introduce an error, especially at the shorter breath-hold time (5 s). However, the 2-s delay would only be observed for the last part of the airway volume; thus the mean delay in the exhalation would be even smaller and is likely to be negligible.
Tissue Phase Concentration
Our estimated mean CawNO in healthy adults is 1,500 ppb (1,439 and 1,503 ppb for air and heliox, respectively), which is more than an order of magnitude larger than that predicted by models that neglect axial diffusion and the shape of the airway tree, but consistent with our previous report using the breath-holding technique (21). This higher concentration approaches that capable of modulating smooth muscle tone. It has recently been demonstrated that soluble guanylate cyclase, the enzyme responsible for smooth muscle dilation, can be activated at NO concentrations as low as 3 ppm (5 nM) (5). Thus, in asthma, in which exhaled NO concentrations can be increased by more than fivefold, airway wall concentrations may reach levels that impact airway and vascular smooth muscle tone.
During a breath hold, the concentration of NO in the airways increases because the concentration in the tissue phase (i.e., wall concentration, CawNO) is larger than the gas phase. For a very long breath-hold time, the gas phase concentration, CNO, would eventually reach CawNO. Our estimated mean CawNO in healthy adults is much larger than the experimentally observed peak concentration of 77 and 62 ppb for air and heliox, respectively, after the largest breath-hold time of 30 s. This observation is consistent with our previous work (6, 21), and the discrepancy is due to two phenomena.
First, the sampling system introduces significant distortion of the observed exhaled profile due to axial dispersion (nonideal flow) of the gas within the mouthpiece assembly and sampling line leading to the NO analyzer. This causes a pulse of NO to be significantly flattened (thus lowering the peak concentration) and broadened without altering the total mass of NO in the peak. This phenomenon was assessed theoretically in previous studies (6, 21), which accounted for axial dispersion within the sampling line. Herein, we have accounted for this effect experimentally (seeAPPENDIX). As presented in Fig. 6, CNO obs(t) predicted by the T-AD model was calculated on the basis of experimental composite responses (n = 20) of NO tracings for both air and heliox as the carrier gas. These results are in good agreement with our experimental measurements for both air and heliox breathing. However, CNO obs(t) predicted by model T-AD generates slightly lower values compared with experimentally observed CNO peak, which may be a consequence of averaging 20 experimental NO tracings to determine the composite responses. Other possible explanations are limitations on the precision of the tracer study (see APPENDIX) or that CawNO could be higher in the upper portion of the airway than in the lower airway (6).
The second reason why the observed gas concentration is less than CawNO is due to the observation that a steady state (or equilibrium) has not been reached with the gas phase. This can be observed by simply noting that CNO peak after a 30-s breath-hold time is significantly larger than that after the 20-s breath-hold time. The estimated mean time to reach 95% of equilibrium (i.e., CawNO) is 128 s (2.13 min) for air and 218 s (3.63 min) for heliox breathing from the T-AD model. The longer time in the presence of heliox is a direct result of the enhanced loss of NO to the alveolar region, resulting in a smaller net (i.e., flux of the airway wall minus flux into the alveolar region) flux of NO into the gas phase.
In conclusion, utilizing a newly developed technique based on progressively increasing breath-hold times, this study investigated the impact of altering the properties of the insufflating gas on airway NO exchange. In the presence of heliox, the rate of NO diffusion is enhanced 2.3-fold and results in enhanced loss of airway NO to the alveolar region. A trumpet model that considers axial diffusion is able to accurately simulate this effect and predict airway NO exchange parameters in healthy adults that characterize airway wall tissue and are independent of the insufflating gas. A result of this model is the determination of airway wall concentrations in healthy adults that exceed 1 ppm, which is approximately an order of magnitude larger than estimates made with models that neglect the trumpet geometry and axial diffusion. This concentration approaches that capable of modulating airway smooth muscle tone and thus may be of clinical interest in disease states such as asthma that have elevated exhaled NO. We conclude that accurate estimation of flow-independent airway NO exchange parameters must include mathematical models that consider axial diffusion of NO in the gas phase and the trumpet shape of the airway tree.
|
APPENDIX |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
We performed a tracer study to investigate distortion of NO exhalation profiles by the sampling system (including the mouthpiece and sampling line). We injected 0.2 ml of 45 ppm NO (at a constant rate, over 1.56 s) into a diluent gas stream (250 ml/s of either air or heliox). We then monitored the output response (solid lines, shown in Fig. A1, representing the averages of 20 NO tracings), which was significantly flattened and broadened for both air (mean CNO peak reduction of 48%, see Fig. A1A) and heliox (mean CNO peak reduction of 42%; see Fig. A1B). These results were sufficient to compute the impulse response (dashed lines shown in Fig. A1, scaled to yield NO masses equal to the pulse inputs for presentation), which relates the observed output response (measured data) to the expected input.
|
25 cm upstream of the sampling line inlet). From the injection point, the diluent gas first passes through an entrance region (50 ml and 2.8-cm diameter) and then enters an in-line filter (40 ml and 7-cm diameter) and traverses an exit region (75 ml and 2.8-cm diameter) before it reaches the sampling point. We assumed that the injected NO was relatively well mixed within the entrance region, upstream of the filter. Thus we approximated the NO profile near the mouthpiece entrance as a 1.56-s duration stepwise, pulse ("square-wave") input (see gray shaded regions of Fig. A1). To check this assumption, we considered alternate orientations for administering the 45 ppm NO into the diluent gas stream and confirmed that these alternatives had little effect upon the measured response. The scaled, impulse responses (shown in Fig. A1) were normalized to compute the "unit impulse responses" (transfer functions) for both air and heliox, on the basis of the tracer experiments. These transfer functions were then applied to the fitted T-AD model to predict the expected output response, on the basis of composite data for all nine subjects (see Fig. 6), which resulted in a potential CNO peak reduction of 6- to 30-fold.
These results should be interpreted prudently, because significant background noise limits resolution of the impulse response, which is based on composite averages of 20 tracer experiments and does not consider variation between these experiments. Furthermore, input NO profiles predicted by the fitted T-AD model are very sharp (0.1- to 0.2-s duration), compared with the tracer experiments (1.56-s duration), which leads to a much more significant reduction in CNO peak. An alternative approach to the tracer results would be to deconvolve the NO concentration vs. time measurements from the breath-hold experiments directly, which would yield experimental estimates of CNO peak values (independent of any pulmonary model).
Finally, our assumption of a square-wave input (which may actually have been distorted at the mouthpiece entrance) for the tracer study could have resulted in overcorrection for CNO peak (the estimated exhaled peaks), on the basis of the fitted T-AD model. This would partially explain some of the discrepancies in Fig. 6, because CNO peak values predicted by the model (i.e., model predictions of peak NO in expired gas after distortion in the sampling system, denoted by solid circles in Fig. 6) are lower than the observed CNO peak values (i.e., observed peak NO in expired gas, denoted by open circles in Fig. 6).
|
GRANTS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
|
ACKNOWLEDGMENTS |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
|
FOOTNOTES |
|---|
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
|
REFERENCES |
|---|
|
TOPABSTRACTMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  S. C. George How accurately should we estimate the anatomical source of exhaled nitric oxide? J Appl Physiol, April 1, 2008; 104(4): 909 - 911. [Full Text] [PDF]
|
|
|
|
 |
|
 K. Roy, Z. L. Borrill, C. Starkey, A. L. Hazel, J. Morris, J. Vestbo, and D. Singh Use of different exhaled nitric oxide multiple flow rate models in COPD Eur. Respir. J., April 1, 2007; 29(4): 651 - 659. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Condorelli, H.-W. Shin, A. S. Aledia, P. E. Silkoff, and S. C. George A simple technique to characterize proximal and peripheral nitric oxide exchange using constant flow exhalations and an axial diffusion model J Appl Physiol, January 1, 2007; 102(1): 417 - 425. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
, Each individual data point from model T; 
, each individual data point from model T-AD. Bars represent the mean. 
Statistically significant difference among models (paired t-test, P < 0.05). *Statistically significant difference between air and heliox (paired t-test, P < 0.05).
, Calculated maximum NO concentration exiting the mouth (CENO; i.e., model prediction of peak NO concentration in expired gas) using mean values for best fit, airway wall parameters for air (DawNO = 3.70 pl·s–1·ppb, CawNO = 1,439 ppb, A) and heliox (DawNO = 3.56 pl·s–1·ppb, CawNO = 1,503 ppb, B); 