Abstract
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.
Highlights
The fractional concentration of exhaled nitric oxide (FeNO) is considered a biomarker for airway inflammation
We developed a unifying framework for a comprehensive set of existing and novel estimators of twocompartment model NO parameters and compared these candidate methods
A novel nonlinear least squares model with natural log-transformation produced unbiased NO parameter estimates with appropriate measures of uncertainty, had excellent fit to the CHS data, and satisfied modeling assumptions. Popular for their simplicity of implementation, linear approximation methods— using the flow rates available in the CHS—relied on an assumption necessary for the linear approximation that was invalid for most CHS participants, produced biased
Summary
The fractional concentration of exhaled nitric oxide (FeNO) is considered a biomarker for airway inflammation. Guidelines have been developed for the standardized measurement of FeNO at a single 50 ml/s exhalation flow rate [6]. At the relatively low flow rate of 50 ml/s, FeNO is primarily from proximal airway sources [7]. Higher flow FeNO provides more information about distal/alveolar sources, but is an imperfect proxy for alveolar NO concentration [8]. Several mathematical models have been developed that describe the physiology of NO in the lower respiratory tract [9,10,11] using parameters that quantify both proximal and distal NO contributions. A simple and widely used two-compartment model uses differential equations to relate FeNO at a constant flow rate (V_ ) to three NO parameters: maximum airway flux (J9awNO), airway tissue diffusing capacity (DawNO), and alveolar NO concentration (CANO) [12]. The closed form solution of this model is: FeNO~Ja0 w NODaw NOz
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