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

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.

SUBMITTER: Eckel SP

PROVIDER: S-EPMC3894971 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveAirway inflammation plays an important role in obstructive sleep apnea (OSA); exhaled nitric oxide is regarded as a noninvasive marker of airway inflammation. The aim of this study was to evaluate fractional exhaled nitric oxide (FeNO) and nasal nitric oxide (nNO) in patients with OSA.MethodsSeventy-five patients with OSA and 30 health controls were enrolled in this study. FeNO and nNO were measured before and after sleep. Nasal lavage was performed in 31 non-smoking individuals immediately after NO measurement in the morning. The sample of nasal lavage was taken for cell classification and analyzing interleukin 6 (IL-6) and interleukin 8 (IL-8).ResultsBoth FeNO and nNO were significantly higher in OSA (before sleep FeNO 21.08 ± 8.79 ppb vs.16.90 ± 6.86 ppb, p = 0.022; after sleep FeNO 25.57 ± 15.58 ppb vs.18.07 ± 6.25 ppb, p = 0.003; before sleep nNO 487.03 ± 115.83 ppb vs. 413.37 ± 73.10 ppb, p = 0.001; after sleep nNO 550.07 ± 130.24 ppb vs. 460.43 ± 109.77 ppb, p < 0.001). Furthermore, in non-smoking OSA, nNO levels were positively correlated with apnea hypopnea index (AHI) and average decrease of pulse arterial oxygen saturation (SpO2); after sleep, nNO was also positively associated to recording time with SpO2 < 90% and negatively associated to minimum SpO2. Both before and after sleep nNO levels were positively correlated with the percentage of neutrophils in nasal lavage (r = 0.528, p = 0.014; r = 0.702, p < 0.001, respectively). Additionally, before sleep nNO was also positively associated with IL-6 (r = 0.586, p = 0.005) and IL-8 (r = 0.520, p = 0.016) concentration.ConclusionThis study sustains the presence of airway inflammation in OSA patients with the increase of FeNO and nNO. The data suggests nNO might have greater value than FeNO since it positively correlated with OSA severity, and nNO is a potential bio-marker of nasal inflammation in non-smoking OSA patients.

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

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