How accurately should we estimate the anatomical source of exhaled nitric oxide?

Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide

Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO—measured non-invasively at the mouth—as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children’s Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.

Modeling of the Transport and Exchange of a Gas Species in Lungs With an Asymmetric Branching Pattern. Application to Nitric Oxide

Over the years, various studies have been dedicated to the mathematical modeling of gas transport and exchange in the lungs. Indeed, the access to the distal region of the lungs with direct measurements is limited and, therefore, models are valuable tools to interpret clinical data and to give more insights into the phenomena taking place in the deepest part of the lungs. In this work, a new computational model of the transport and exchange of a gas species in the human lungs is proposed. It includes (i) a method to generate a lung geometry characterized by an asymmetric branching pattern, based on the values of several parameters that have to be given by the model user, and a method to possibly alter this geometry to mimic lung diseases, (ii) the calculation of the gas flow distribution in this geometry during inspiration or expiration (taking into account the increased resistance to the flow in airways where the flow is non-established), (iii) the evaluation of the exchange fluxes of the gaseous species of interest between the tissues composing the lungs and the lumen, and (iv) the computation of the concentration profile of the exchanged species in the lumen of the tracheobronchial tree. Even if the model is developed in a general framework, a particular attention is given to nitric oxide, as it is not only a gas species of clinical interest, but also a gas species that is both produced in the walls of the airways and consumed within the alveolar region of the lungs. First, the model is presented. Then, several features of the model, applied to lung geometry, gas flow and NO exchange and transport, are discussed, compared to existing works and notably used to give new insights into experimental data available in the literature, regarding diseases, such as asthma, cystic fibrosis, and chronic obstructive pulmonary disease.

Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model

Exhaled nitric oxide (FeNO) is an established respiratory biomarker with clinical applications in the diagnosis and management of asthma. Because FeNO depends strongly on the flow (exhalation) rate, early protocols specified that measurements should be taken when subjects exhaled at a fixed rate of 50 ml/s. Subsequently, multiple flow (or "extended") protocols were introduced which measure FeNO across a range of fixed flow rates, allowing estimation of parameters including Caw NO and CA NO which partition the physiological sources of NO into proximal airway wall tissue and distal alveolar regions (respectively). A recently developed dynamic model of FeNO uses flow-concentration data from the entire exhalation maneuver rather than plateau means, permitting estimation of Caw NO and CA NO from a wide variety of protocols. In this paper, we use a simulation study to compare Caw NO and CA NO estimation from a variety of fixed flow protocols, including: single maneuvers (30, 50,100, or 300 ml/s) and three established multiple maneuver protocols. We quantify the improved precision with multiple maneuvers and the importance of low flow maneuvers in estimating Caw NO. We conclude by applying the dynamic model to FeNO data from 100 participants of the Southern California Children's Health Study, establishing the feasibility of using the dynamic method to reanalyze archived online FeNO data and extract new information on Caw NO and CA NO in situations where these estimates would have been impossible to obtain using traditional steady-state two compartment model estimation methods.

Bayesian estimation of physiological parameters governing a dynamic two-compartment model of exhaled nitric oxide

The fractional concentration of nitric oxide in exhaled breath (fe_NO) is a biomarker of airway inflammation with applications in clinical asthma management and environmental epidemiology. fe_NO concentration depends on the expiratory flow rate. Standard fe_NO is assessed at 50 mL/sec, but “extended NO analysis” uses fe_NO measured at multiple different flow rates to estimate parameters quantifying proximal and distal sources of NO in the lower respiratory tract. Most approaches to modeling multiple flow fe_NO assume the concentration of NO throughout the airway has achieved a “steady‐state.” In practice, this assumption demands that subjects maintain sustained flow rate exhalations, during which both fe_NO and expiratory flow rate must remain constant, and the fe_NO maneuver is summarized by the average fe_NO concentration and average flow during a small interval. In this work, we drop the steady‐state assumption in the classic two‐compartment model. Instead, we have developed a new parameter estimation approach based on measuring and adjusting for a continuously varying flow rate over the entire fe_NO maneuver. We have developed a Bayesian inference framework for the parameters of the partial differential equation underlying this model. Based on multiple flow fe_NO data from the Southern California Children's Health Study, we use observed and simulated NO concentrations to demonstrate that our approach has reasonable computation time and is consistent with existing steady‐state approaches, while our inferences consistently offer greater precision than current methods.

Optimal flow rate sampling designs for studies with extended exhaled nitric oxide analysis

INTRODUCTION

The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation. Repeat FeNO maneuvers at multiple fixed exhalation flow rates (extended NO analysis) can be used to estimate parameters quantifying proximal and distal sources of NO in mathematical models of lower respiratory tract NO. A growing number of studies use extended NO analysis, but there is no official standard flow rate sampling protocol. In this paper, we provide information for study planning by deriving theoretically optimal flow rate sampling designs.

METHODS

First, we reviewed previously published designs. Then, under a nonlinear regression framework for estimating NO parameters in the steady-state two compartment model of NO, we identified unbiased optimal four flow rate designs (within the range of 10-400 ml s^-1) using theoretical derivations and simulation studies. Optimality criteria included NO parameter standard errors (SEs). A simulation study was used to estimate sample sizes required to detect associations with NO parameters estimated from studies with different designs.

RESULTS

Most designs (77%) were unbiased. NO parameter SEs were smaller for designs with: more target flows, more replicate maneuvers per target flow, and a larger range of target flows. High flows were most important for estimating alveolar NO concentration, while low flows were most important for the proximal NO parameters. The Southern California Children's Health Study design (30, 50, 100 and 300 ml s^-1) had ≥1.8 fold larger SEs and required 1.1-3.2 fold more subjects to detect the association of a determinant with each NO parameter as compared to an optimal design of 10, 50, 100 and 400 ml s^-1.

CONCLUSIONS

There is a class of reasonable flow rate sampling designs with good theoretical performance. In practice, designs should be selected to balance the tradeoffs between optimality and feasibility of the flow range and total number of maneuvers.

Exhaled Nitric Oxide Levels Among Adults With Excessive Alcohol Consumption

BACKGROUND

More than one-quarter of the US population qualify as excessive alcohol consumers. Alcohol use impacts several lung diseases, and heavy consumption has been associated with poor clinical outcomes. The fractional excretion of exhaled nitric oxide (Feno) has clinical implications in multiple airways diseases. We hypothesized that excessive alcohol intake is associated with lower Feno levels.

METHODS

To test this hypothesis, we examined a sample consisting of 12,059 participants, aged 21 to 79 years, interviewed between 2007 and 2012 from the National Health and Examination Survey. Two valid Feno measurements that were reproducible were recorded. Alcohol questionnaire data were used to define the following alcohol groups: never drinkers, nonexcessive drinkers, excessive drinkers, and former excessive drinkers. The natural logarithm of Feno values [ln(Feno)] as well as blood eosinophil count and C-reactive protein were used as dependent variables to test the association with alcohol groups including multivariable linear regression models with adjustment for predictors of Feno.

RESULTS

Excessive alcohol consumption comprised 3,693 (26.9%) of the US sample population. Controlling for all other factors, excessive alcohol consumption had a negative association and was an independent predictor for ln(Feno) levels in comparison with the never-drinker group (-0.11; 95% CI, -0.17 to -0.06; P < .001). ln(Feno) levels decreased across categories of increasing alcohol use (P < .001).

CONCLUSIONS

Accounting for alcohol use in the interpretation of Feno levels should be an additional consideration, and further investigations are warranted to explore the complex interaction between alcohol and nitric oxide in the airways.

Estimation of parameters in the two-compartment model for exhaled nitric oxide

The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation that is being increasingly considered in clinical, occupational, and epidemiological applications ranging from asthma management to the detection of air pollution health effects. FeNO depends strongly on exhalation flow rate. This dependency has allowed for the development of mathematical models whose parameters quantify airway and alveolar compartment contributions to FeNO. Numerous methods have been proposed to estimate these parameters using FeNO measured at multiple flow rates. These methods—which allow for non-invasive assessment of localized airway inflammation—have the potential to provide important insights on inflammatory mechanisms. However, different estimation methods produce different results and a serious barrier to progress in this field is the lack of a single recommended method. With the goal of resolving this methodological problem, we have developed a unifying framework in which to present a comprehensive set of existing and novel statistical methods for estimating parameters in the simple two-compartment model. We compared statistical properties of the estimators in simulation studies and investigated model fit and parameter estimate sensitivity across methods using data from 1507 schoolchildren from the Southern California Children's Health Study, one of the largest multiple flow FeNO studies to date. We recommend a novel nonlinear least squares model with natural log transformation on both sides that produced estimators with good properties, satisfied model assumptions, and fit the Children's Health Study data well.

Basal and induced NO formation in the pharyngo-oral tract influences estimates of alveolar NO levels

The present study analyzed how models currently used to distinguish alveolar from bronchial contribution to exhaled nitric oxide (NO) are affected by manipulation of NO formation in the pharyngo-oral tract. Exhaled NO was measured at multiple flow rates in 15 healthy subjects in two experiments: 1) measurements at baseline and 5 min after chlorhexidine (CHX) mouthwash and 2) measurements at baseline, 60 min after ingestion of 10 mg NaNO3/kg body wt, and 5 min after CHX mouthwash. Alveolar NO concentration (CalvNO) and bronchial flux (J′awNO) were calculated by using the slope-intercept model with or without adjustment for trumpet shape of airways and axial diffusion (TMAD). Salivary nitrate and nitrite were measured in the second experiment. CalvNO [median (range)] was reduced from 1.16 ppb (0.77, 1.96) at baseline to 0.84 ppb (0.57, 1.48) 5 min after CHX mouthwash ( P < 0.001). The TMAD-adjusted CalvNO value after CHX mouthwash was 0.50 ppb (0, 0.85). The nitrate load increased J′awNO from 32.2 nl/min (12.2, 60.3) to 57.1 nl/min (22.0, 119) in all subjects and CalvNO from 1.47 ppb (0.73, 1.95) to 1.87 ppb (10.85, 7.20) in subjects with high nitrate turnover (>10-fold increase of salivary nitrite after nitrate load). CHX mouthwash reduced CalvNO levels to 1.15 ppb (0.72, 2.07) in these subjects with high nitrate turnover. All these results remained consistent after TMAD adjustment. We conclude that estimated alveolar NO concentration is affected by pharyngo-oral tract production of NO in healthy subjects, with a decrease after CHX mouthwash. Moreover, unknown ingestion of dietary nitrate could significantly increase estimated alveolar NO in subjects with high nitrate turnover, and this might be falsely interpreted as a sign of peripheral inflammation. These findings were robust for TMAD.