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PLoS One. 2014 Jan 17;9(1):e85471. doi: 10.1371/journal.pone.0085471. eCollection 2014.

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

Author information

  • 1Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America.
  • 2Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America.

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.

PMID:
24465571
[PubMed - indexed for MEDLINE]
PMCID:
PMC3894971
Free PMC Article
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