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INVITED REVIEW

Modeling pulmonary nitric oxide exchange

¹Department of Chemical Engineering and Materials Science and ²Department of Biomedical Engineering, University of California, Irvine, California 92697-2575; ³Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden; ⁴The Johns Hopkins Asthma and Allergy Center, Baltimore, Maryland 21224; and ⁵National Jewish Medical and Research Center, Denver, Colorado 80206

	ABSTRACT

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
Nitric oxide (NO) was first detected in the exhaled breath more than a decade ago and has since been investigated as a noninvasive means of assessing lung inflammation. Exhaled NO arises from the airway and alveolar compartments, and new analytical methods have been developed to characterize these sources. A simple two-compartment model can adequately represent many of the observed experimental observations of exhaled concentration, including the marked dependence on exhalation flow rate. The model characterizes NO exchange by using three flow-independent exchange parameters. Two of the parameters describe the airway compartment (airway NO diffusing capacity and either the maximum airway wall NO flux or the airway wall NO concentration), and the third parameter describes the alveolar region (steady-state alveolar NO concentration). A potential advantage of the two-compartment model is the ability to partition exhaled NO into an airway and alveolar source and thus improve the specificity of detecting altered NO exchange dynamics that differentially impact these regions of the lungs. Several analytical techniques have been developed to estimate the flow-independent parameters in both health and disease. Future studies will focus on improving our fundamental understanding of NO exchange dynamics, the analytical techniques used to characterize NO exchange dynamics, as well as the physiological interpretation and the clinical relevance of the flow-independent parameters.

NO; model; airways; alveoli; inflammation

Because NO exchange dynamics are significantly different from other well-studied gases such as carbon dioxide, new models and analytical methods have been developed to understand the underlying physiology and gas exchange mechanisms. A simple two-compartment model of the lungs has been described by several research groups (17, 34, 44, 48) that adequately explains many of the unique features of NO exchange dynamics, in particular the dependence on exhalation flow rate. The two-compartment model describes exhaled NO arising from two compartments, the airways and the alveolar region, using three flow-independent exchange parameters: one describing the alveolar region (the steady-state NO alveolar concentration), and two describing the airway region (airway NO diffusing capacity and either the maximum airway wall NO flux or the airway wall NO concentration). With the use of these three parameters, the two-compartment model can then predict the exhaled concentration at any desired exhalation flow rate. It is important to note that the two-compartment model does not consider the nasal compartment and the significant NO production in the paranasal sinuses (28). Thus special precautions to close the soft palate during exhalation must be made to avoid nasal contamination when model predictions are compared to experimental data.

Since the original description of the two-compartment model, research has focused on three areas: 1) the development of experimental breathing and analytical techniques to accurately and reproducibly estimate the flow-independent NO exchange parameters, 2) the estimation of the flow-independent NO parameters in health and disease, and 3) the further development and testing of the underlying assumptions in the simple two-compartment model. These research thrusts have enhanced our understanding of NO exchange mechanisms, as well as the pathophysiological interpretation of the flow-independent NO exchange parameters.

Glossary

C_NO(V) or F_NO(V)

Fractional concentration of NO in the gas phase within the airway compartment [parts/billion (ppb)]

CA_NO(t) or FA_NO(t)

Mixed or average fractional concentration of NO in the gas phase of the alveolar region (ppb). This concentration may depend on time during a single breath or during the tidal breathing cycle. A steady-state concentration is achieved for breath hold or exhalation times of > 10 s

Calv_NO or Falv_NO

Mean alveolar tissue concentration of NO equivalent to the steady-state concentration in the gas phase of the alveolar compartment during a breath hold or exhalation of > 10 s

CE_NO or FE_NO

Fractional exhaled concentration of NO, which may be a function of time, exhaled volume, and exhalation flow rate (ppb)

CE_NO,i or FE_NO,i

Fractional exhaled concentration of NO in phase III of the exhalation profile at constant exhalation flow rate i (ppb)

Caw_NO or Faw_NO

Mean (by radial position) airway tissue concentration of NO (wall concentration) equivalent to the steady-state concentration in the gas phase of the airway compartment during a prolonged breath hold (ppb)

Daw_NO

Global or total airway compartment diffusing capacity, transfer factor, or conductance for radial mass transfer of NO from the airway wall to the gas stream (pl·ppb^—1·s^—1)

Dalv_NO

Global or total alveolar compartment diffusing capacity, transfer factor, or conductance for mass transfer of NO across the alveolar membrane (pl·ppb^—1·s^—1)

J'aw_NO

Global or total maximum flux (rate of radial transport) of NO in the airway compartment (pl/s), which occurs as the gas phase concentration in the airway compartment approaches zero, and is equal to the product Daw_NO x Caw_NO

Jaw_NO

Global or total flux of NO in the airway compartment (pl/s). This flux may depend on time during a single breath or tidal breathing cycle and approaches J'aw_NO for exhalation flow rates of >50 ml/s in healthy adults

J'alv_NO

Global or total maximum flux of NO in the alveolar compartment (pl/s)

Vaw

Volume of the airway compartment exchanging NO, approximately equivalent to the anatomic dead space (ml)

VA

Volume of the alveolar compartment that depends on exhalation time and volume (ml)

NO

Elimination rate of NO from the breath during exhalation (ml/s)

e

Exhalation flow rate (ml/s)

	TWO-COMPARTMENT MODEL

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
The two-compartment model has been described in detail in prior publications (17, 34, 44, 48), and only the salient features will be described herein. Figure 1 depicts the basic features of the model. The alveolar NO concentration, which probably changes in a cyclical manner with respiration, reflects the balance between NO produced locally or inhaled and NO destroyed or diffusing away. During an exhalation or breath hold of more than 8–10 s (3, 13, 21, 47–49, 52), the concentration in the alveolar region, CA_NO, reaches a steady-state concentration. As alveolar air is convected through the airways toward the mouth during exhalation, the gas stream is conditioned with NO diffusing from the airway wall. Briefly, the amount of NO absorbed by the airstream from the airways per unit time is referred to as the flux of NO from the airways, Jaw_NO (pl/s), and is expressed as a linear function of the airway gas phase NO concentration, C_NO, by the following (48)

(1)

or

(2)

View larger version (18K): [in this window] [in a new window]   Fig. 1. Schematic of 2-compartment model used to describe nitric oxide (NO) exchange dynamics. Exhaled NO concentration (CENO) is the sum of 2 contributions, the alveolar region and the airway region, which depends on 3 flow-independent parameters: maximum total volumetric flux of NO from the airway wall (J'awNO, pl/s), diffusing capacity of NO in the airways (DawNO, pl·s-1·ppb-1), and steady-state alveolar concentration (CANO, ppb). JawNO, total flux (pl/s) of NO between the tissue and gas phase in the airway and is an inverse function of the exhalation flow rate (E); CNO, concentration of NO in the gas phase within the airway compartment.

J'aw_NO is the maximum flux of NO from the airway tissue, which is approximately equal to the airway compartment flux if C_NO were zero, or, alternatively, simply the product Daw_NO x Caw_NO (see DETERMINING FLOW-INDEPENDENT NO PARAMETERS). Daw_NO is the airway NO diffusing capacity (conductance for mass transfer or transfer factor) for radial transport of NO between the airway tissue and the gas phase.

Once the flow-independent parameters are known, the two-compartment model can be used to predict the exhaled concentration of NO, CE_NO (ppb) (or FE_NO) at any constant exhalation flow (E) by using the relatively simple exponential expression (17, 34, 44, 48)

(3)

Our laboratory has previously demonstrated that CE_NO predicted by Eq. 3 is no different than the experimentally measured CE_NO in phase III of the exhalation profile in healthy adults (37–39), with the advantage that intersubject and interpopulation variations in flow rate can be accounted for by calculating CE_NO at a precise desired flow rate (e.g., 50 ml/s).

	DETERMINING FLOW-INDEPENDENT NO PARAMETERS

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
The potential of the flow-independent NO parameters lies in their ability to partition exhaled NO into two important anatomic subdivisions of the lungs, the airways and the alveolar region, and also provide both structural and metabolic information about the airways relevant to the NO pathways. The challenge has been to develop robust breathing techniques and analytical methods to accurately and reliably estimate their values in healthy adults and children and in those with lung disease. To uniquely determine the airway and alveolar contribution to exhaled NO, multiple exhalation flow rates must be sampled (17, 34, 44, 48). Two general approaches have been described in the literature: multiple constant exhalation flows and a dynamically changing flow within a single exhalation.

Multiple constant exhalation flows. Several groups have manipulated the governing equations of the two-compartment model to arrive at analytical expressions that can be used in conjunction with multiple constant exhalation flows to estimate one or more of the flow-independent NO exchange parameters. When E is large compared with Daw_NO, the exponential function of Eq. 3 approaches the first-order linear approximation [i.e., exp(-Daw_NO/E) = 1 - Daw_NO/E]. This approximation occurs for E > 5 x Daw_NO ml/s or 50 ml/s in healthy adults (see mean values of Daw_NO in healthy adults in Table 1). Under this condition, Eq. 3 reduces to the following simple expression

(4)

Under most circumstances (see Tables 1 and 2), CA_NO is normally <2% of Caw_NO and can thus be neglected from the second term on the right-hand side of Eq. 4. Making this simplification and recognizing that J'aw_NO = Daw_NO x Caw_NO, the following simple expression for CE_NO is derived

(5)

Then, if both sides of Eq. 5 are multiplied by E, the following expression for the elimination rate of NO, _NO (CE_NO·E, ml NO/s), is derived

(6)

Tsoukias et al. ("Tsoukias" technique) (51) used Eq. 6 to estimate CA_NO and J'aw_NO by measuring _NO at multiple constant exhalation flow rates. As shown in Fig. 2A, the slope and intercept of the resulting linear relationship provides an estimate of CA_NO and J'aw_NO, respectively. Note that in some disease states (e.g., cystic fibrosis, Table 2), CA_NO is larger than 2% of Caw_NO and thus the intercept should be rigorously interpreted as Daw_NO x (Caw_NO - CA_NO), as described earlier.

View larger version (21K): [in this window] [in a new window]   Fig. 2. Schematics depicting 4 different analytical techniques that have been described in the literature to estimate the flow-independent NO parameters by using a minimum of 2 constant exhalation flow rates and a maximum of 3. A: Tsoukias technique. B: Pietropaoli technique. C: Silkoff technique. D: Hogman technique. Circles have been produced by the analytical solution of the 2-compartment model (Eq. 3) by using representative values for the flow-independent parameters (DawNO = 5 pl·s-1·ppb-1; J'awNO = 750 pl/s; CANO = 3 ppb) at 5 different exhalation flow rates: 5, 17.2, 38.2, 100, and 500 ml/s. , Flow rates used by each of the techniques to estimate 2 or more of the flow-independent parameters only in linear regression; , flow rate of the 2-compartment model. Dashed lines, main feature of each technique (described in greater detail in the text).

Pietropaoli et al. ("Pietropaoli" technique) (34) followed with a similar technique to estimate J'aw_NO and CA_NO but also included the estimate of Daw_NO. They used Eq. 5 (rather than Eq. 6) and plotted CE_NO vs. 1/E for E > 200 ml/s and used the intercept as an estimate of CA_NO. Then, using this value for CA_NO, combined with additional flow rates as low as 6 ml/s, they used nonlinear regression techniques (steepest descent and Newton's algorithm) and Eq. 3 to estimate J'aw_NO and Daw_NO. Figure 2B demonstrates a plot of CE_NO vs. 1/E where the intercept is CA_NO and the slope is J'aw_NO (Eq. 5).

Silkoff et al. ("Silkoff" 9-flow technique) (44) utilized a nonlinear regression technique using Eq. 3 and nine different flows (range 4–1,550 ml/s) to estimate all three flow-independent parameters. Then they demonstrated that Daw_NO and J'aw_NO (and hence Caw_NO) could be estimated from two low flow rates (E < 50 ml/s, Silkoff 2-flow technique) by using the slope (-Daw_NO) and intercept (J'aw_NO/Daw_NO) of a plot of _NO vs. CE_NO (Fig. 2C).

Hogman and colleagues ("Hogman" technique) (17, 19) followed Silkoff with a slightly modified technique that combined the technique of Tsoukias and also the concepts of Pietropaoli and Silkoff. In this technique, only three exhalation flow rates are needed, one very low (5 ml/s), one medium flow around 100 ml/s, and a high flow that needs to be 300 ml/s or above but not over 500 ml/s. A line is regressed through the medium and high flow rates to determine CA_NO as described in the Tsoukias technique; then Daw_NO and J'aw_NO are determined from all three flows by use of an iterative technique (Fig. 2D) that uses the second-order term to minimize the error induced from the first-order linear approximation of Eq. 4. Thus three unknown parameters are determined from the minimum number of data points (i.e., three).

The advantage of the Tsoukias and Pietropaoli techniques is the use of only two exhalation flow rates, which are easily performed by most subjects, and the very simple analytical method of analysis. The primary disadvantage is that Daw_NO cannot be determined. The Silkoff 2-flow technique also utilizes only two exhalation flow rates, albeit lower flow rates (<50 ml/s). It may be difficult for some subjects with compromised lung function to continue exhaling long enough at these low flows. To exhale the initial airway volume of 200 ml and achieve a 3-s plateau, a subject needs to exhale for a minimum of 15 s at an exhalation flow rate of 17.2 ml/s. In addition, the Silkoff 2-flow technique does not provide data to determine CA_NO, but the significant advantage of the Silkoff 2-flow technique is the estimation of Daw_NO, which has been shown to be elevated in asthma and independent of steroid therapy (38, 44). The technique of Hogman has the obvious advantage that all of the flow-independent NO exchange parameters can be determined, at the cost of one additional low exhalation flow rate (which may be difficult for some subjects to complete as described above) and a nonlinear regression technique that is numerically more complex.

Dynamically changing flow rate. To avoid performing multiple constant exhalation flow rates, the exhalation flow rate during a single exhalation maneuver can be dynamically altered to sample essentially an infinite (depends on the sampling frequency) number of exhalation flow rates. This strategy was employed in a second technique by Tsoukias et al. ("Tsoukias SB" technique, where SB stands for "single breath") (50). To employ this strategy, the residence time, _res, of each exiting differential bolus of air must be known. Then, from Eq. 3, the exhaled concentration, CE_NO, is simply

(7)

where Vaw is the airway compartment volume. Note the similarity between Eq. 7 and Eq. 3, with the notable exception that E has been replaced with Vaw/_res; hence, this technique not only requires _res, but also requires one to estimate Vaw. Tsoukias et al. (50) used backward integration of the exhalation flow rate signal to determine _res at each exhalation time point and the subjects' body weight in pounds plus age in years to estimate Vaw in units of milliliters.

As described earlier, both low (<50 ml/s) and high (>100 ml/s) exhalation flow rates must be sampled to estimate all of the flow-independent exhalation NO parameters. In a single exhalation maneuver, Tsoukias et al. (50) used a preexpiratory breath hold of 20 s followed by a decreasing exhalation maneuver (300 to 50 ml/s for adults). The result is an exhalation profile that has a large peak early in the exhalation due to the breath hold and accumulation of NO in the airway compartment followed by a progressively increasing NO concentration in phase III. Phases I and II are sensitive to Daw_NO (low flow, actually a zero flow from the breath hold) and J'aw_NO, whereas phase III is sensitive to CA_NO and J'aw_NO. Hence, all three parameters can be uniquely determined (Fig. 3) from a nonlinear least squares algorithm. The primary advantage of this technique is that only a single breathing maneuver is needed and a precise exhalation flow rate is unnecessary (one only needs to record the exhalation flow rate). The primary disadvantages are that some subject populations (young children or those with compromised lung function) cannot perform the 20-s breath hold, the parameter estimation algorithm is numerically complex and thus less portable, and Vaw must be estimated for the analysis. A summary of the analytical techniques to estimate the flow-independent NO exchange parameters is provided in Table 1.

View larger version (13K): [in this window] [in a new window]   Fig. 3. The 2-compartment model prediction of the exhaled NO profile is shown for a single exhalation maneuver with a 20-s preexpiratory breath hold and a decreasing exhalation flow rate. Representative values for lung volumes of a healthy adult have been used, and the "control" values for the flow-independent parameters are DawNO = 5 pl·s-1·ppb-1 (A); J'awNO = 750 pl/s (B); CANO = 3 ppb (C). In each panel, the control profile (solid line) is shown together with the exhaled profile when 1 of the flow-independent parameters is doubled (dashed line). In A, the decreasing exhalation flow rate is also shown on the second y-axis. This informal sensitivity analysis demonstrates graphically which part of the profile is impacted by each parameter. It can be seen that each parameter uniquely impacts the exhaled profile and can thus be uniquely determined. Note that DawNO primarily impacts phases I and II, CANO impacts primarily phase III, whereas J'awNO impacts all 3 phases.

	FLOW-INDEPENDENT NO PARAMETERS IN HEALTH AND DISEASE

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
The techniques described above have been used to estimate values for the flow-independent NO exchange parameters in healthy adults, children, and infants, as well as several diseases including asthma (19, 25–27, 32, 38, 44), allergic alveolitis (25, 27), cystic fibrosis (40), scleroderma (12), allergic rhinitis (19), chronic obstructive pulmonary disease (COPD) (19), Sjogrens syndrome (16), and smoking (18, 19). Tables 2 and 3 summarize these values including the size of the sample and the standard deviation of the estimated mean. Because the techniques utilized have unique sources of error, absolute values for the flow-independent NO exchange parameters may vary between studies for the same group of subjects. Hence, Tables 2 and 3 also include the technique utilized in each study.

In healthy adults, a range of 1.0–5.6 ppb for CA_NO has been reported, which is consistent with the direct bronchoscopic measurements of the lower airways (8, 9). Although direct measurement of the alveolar maximum flux, J'alv_NO, is not possible, an estimate can be made by using reports of the alveolar diffusing capacity, Dalv_NO (also reported as DL_NO), and the simple relationship at steady state, J'alv_NO = Dalv_NO x CA_NO. Reported ranges for DL_NO in healthy adults at total lung capacity (alveolar volumes of 5–7 liters) are 1,580–2,100 pl·s^-1·ppb^-1 using several different techniques (4, 13, 33, 47, 52). This provides a range of 1,580–11,760 pl/s for J'alv_NO. For J'aw_NO, Daw_NO, and Caw_NO, the ranges in healthy adults are 420–1,280 pl/s, 3.1–9.2 pl·s^-1·ppb^-1, and 75–225 ppb, respectively. Note that Dalv_NO and J'alv_NO are significantly larger than Daw_NO and J'aw_NO, respectively. This is likely due to the enormous surface area in the alveolar region; however, note that the tissue concentration is much larger in the airways (ratio of maximum flux and diffusing capacity).

In disease states, there are several interesting trends, many of which are consistent with our present understanding of regional inflammation in these diseases. Although asthma is a heterogeneous disease, it appears that steroid-treated subjects with asthma have an elevated Daw_NO, whereas subjects with asthma who are not treated with steroids have elevated Daw_NO, J'aw_NO, and possibly CA_NO. Subjects with allergic alveolitis and Sjogrens have elevated CA_NO, whereas subjects with scleroderma have an elevated CA_NO but a reduced J'aw_NO. Subjects with cystic fibrosis have an elevated Daw_NO but a reduced Caw_NO. Allergic rhinitis appears to increase Daw_NO. For subjects with COPD, CA_NO is elevated, whereas Daw_NO is elevated in one group and decreased in another group relative to healthy controls, suggesting distinct subpopulations in NO gas exchange dynamics. In smoking, Caw_NO and J'aw_NO are both reduced.

	FUTURE DIRECTIONS

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
Future progress in our understanding of NO exchange dynamics and potential clinical applicability will depend on our ability to improve our basic understanding of the chemical and physical factors that impact NO gas exchange, understanding limitations of the two-compartment model, improving analytical and experimental models, and completing additional cross-sectional and longitudinal clinical studies. Additionally, practical measurement methods that are applicable to a wide range of ages and disease processes will be necessary.

The strength of the two-compartment model is its relative simplicity in characterizing NO exchange dynamics with only three flow-independent parameters. However, this simplicity comes at the cost of several major simplifying assumptions that make the governing equations tractable. Characterizing the lungs with only two compartments is obviously a gross simplification. The transition region between the bronchioles and the alveoli (terminal and respiratory bronchioles) is not described in the two-compartment model. This is evident by the abrupt change in concentration between the emptying of the airway and the alveolar compartment in the model predictions shown in Fig. 3 for the single-breath maneuver with a preexpiratory breath hold. The experimental exhaled NO tracings for a similar maneuver produce a smooth rise and fall as the airway compartment is emptied during exhalation. The analytical technique to estimate the flow-independent NO parameters compensates for this model limitation by predicting the total volume of NO exhaled in phases I and II rather than predicting the precise shape of this portion of the tracing.

Another model simplification is that axial diffusion of NO in the gas phase as a mechanism of transport of NO is neglected. It has recently been demonstrated theoretically that if a substantial source of NO arises in the small airways, neglecting axial diffusion may cause the two-compartment model to significantly underestimate the flux of NO from the airways due to back-diffusion into the alveolar region (36, 53). Future work must focus on experimental validation of such predictions and also on the progressive development and validation of the two-compartment simplifications.

One area in which the analytical methods could be substantially improved is in the analysis of exhaled NO tracings during normal tidal breathing. The expired NO concentration profile during tidal breathing has been investigated in infants (15), but partitioning the exhaled NO into airway and alveolar compartments was not accomplished. In adults, all of the techniques to date utilize a single-breath maneuver, which can be cumbersome for some subject populations such as infants or those with compromised lung function. Several challenges must be overcome with tidal breathing. Changes in lung volume for tidal breathing (400–800 ml) are smaller than for single-breath maneuvers (1,500–3,000 ml). A single cycle (inhalation and exhalation) occurs over a relatively short time frame (4–8 s), and exhalation profiles are observed in a narrow window (2–4 s). Over the short exhalation, there is insufficient time to accumulate appreciable amounts of NO in the airway and alveolar compartments. Hence, tidal breathing profiles are flatter and lack the easily recognizable characteristics of single-breath maneuvers. Furthermore, expired NO levels for tidal breathing are roughly fourfold lower (5–10 ppb) than those observed for single-breath maneuvers. Some of these obstacles can be overcome by observing a series of sequential tidal breaths, whereas others will need to be addressed with improved analytical instrumentation and data-filtering strategies to improve the signal-to-noise ratio.

New experimental methods, techniques, and protocols will also be necessary to complete our understanding of NO exchange dynamics in the lungs. Our present understanding of NO metabolism and transport within lung tissue remains in its infancy. It is clear that exhaled NO is derived from several isoforms of NO synthase in the lungs (54, 55); however, NO also undergoes many chemical reactions that both consume and secondarily produce NO. The substrates for these reactions are numerous and include and S-nitrosothiols, proteins, superoxide, and oxygen (10, 41). The models to describe NO exchange utilize a lumped first-order rate constant and distribute production and consumption uniformly throughout the airway tree. However, there is evidence that NO production is heterogeneous in the airways with a relatively larger production in the upper airways (7, 42). These simplifications will need to be addressed with more advanced experiments such as the use of inhaled agents that can potentially map NO production and consumption within the lungs (5, 6, 24, 35, 45, 56, 57).

Although there have been numerous studies that have addressed changes in the flow-independent NO parameters in disease, applications in the clinical assessment and treatment of patients will require numerous additional studies. The focus of these studies should include repeatability with different analytical techniques, controlled treatment interventions, and longitudinal analysis of the parameters. For example, it has been proposed that Daw_NO, which depends on physical and anatomical determinants in the airways such as gas solubility and airway surface area, may be impacted by the well-documented structural and physiological changes termed "airway remodeling" in asthma that occur over the time frame of years (38, 44). In contrast, although J'aw_NO and Caw_NO also depend on structural and physical determinants in the airways, they also depend on the production rate of NO in the airway tissue (48). Hence, they may be sensitive to more acute interventions in asthma such as corticosteroid therapy, which impacts the pathophysiology of asthma over the time frame of days to weeks (26, 44). More advanced clinical studies are needed to address these possible applications of NO exchange models.

	SUMMARY

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
Exhaled NO has multiple sources in the lungs, and thus new analytical techniques have been developed to capture this rich feature. To date, a two-compartment (airways and alveoli) model of the lungs has proven to be a simple and robust means of describing NO exchange dynamics. The two compartments can be described by three flow-independent NO parameters, two of which characterize the airway compartment (Daw_NO and J'aw_NO or Caw_NO) and one which characterizes the alveolar compartment (CA_NO). Several analytical techniques have been employed to estimate these parameters in both health and disease. The techniques utilize either multiple vital capacity maneuvers at two or more constant exhalation flow rates or a single vital capacity maneuver in which the flow rate is dynamically altered during the exhalation. Although future studies must address several important model simplifications, early reports suggest the flow-independent NO parameters are uniquely altered in several disease states such as asthma, cystic fibrosis, scleroderma, alveolitis, COPD, and allergic rhinitis and thus may provide pathophysiological insight or assist in the clinical management of inflammatory lung diseases.

	ACKNOWLEDGMENTS

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 
GRANTS

This work was supported in part by National Heart, Lung, and Blood Institute Grant HL-60636.

	FOOTNOTES

 

Address for reprint requests and other correspondence: S. C. George, Dept. of Chemical Engineering and Materials Science, 916 Engineering Tower, Univ. of California, Irvine, CA 92697-2575 (E-mail: scgeorge{at}uci.edu).

	REFERENCES

TOP ABSTRACT TWO-COMPARTMENT MODEL DETERMINING FLOW-INDEPENDENT NO... FLOW-INDEPENDENT NO PARAMETERS... FUTURE DIRECTIONS SUMMARY ACKNOWLEDGMENTS REFERENCES

 

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