Dataset Information

Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections.

ABSTRACT: The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronically infected CF patients p = 0.044. We confirmed the previously established discriminative power of exhaled breath analysis in separation between healthy subjects and patients with CF or PCD. Furthermore, this method significantly discriminates CF patients suffering from a chronic pulmonary P. aeruginosa (PA) infection from CF patients without a chronic pulmonary infection. Further studies are needed for verification and to investigate the role of electronic nose technology in the very early diagnostic workup of pulmonary infections before the establishment of a chronic infection.

SUBMITTER: Joensen O

PROVIDER: S-EPMC4277311 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections.

Joensen Odin O Paff Tamara T Haarman Eric G EG Skovgaard Ib M IM Jensen Peter Ø PØ Bjarnsholt Thomas T Nielsen Kim G KG

PloS one 20141226 12

The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection statu ...[more]

PMID: 25542036

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Project description:BackgroundA correlation exists between the microbial flora of the upper and lower airways in patients with cystic fibrosis (CF) or with primary ciliary dyskinesia (PCD). The sinuses can function as a bacterial reservoir where gram-negative bacteria adapt to the airways and repeatedly are aspirated to and colonize the lungs according to the theory of the united (unified) airways. Whereas the pattern of bacterial flora in the lower airways has been extensively studied, the upper airways have drawn limited attention.ObjectiveOur aim was to review the literature that reported bacterial flora in the sinuses and nasal cavities of patients with CF or PCD.MethodsA number of medical literature data bases were systematically searched between January 1960 and July 2016. We applied the following inclusion criteria: a minimum of one case of PCD (or Kartagener syndrome) or CF, and microbiology analyses from the nose or paranasal sinuses.ResultsWe included 46 studies (1823 patients) from 16 countries. Staphylococcus aureus was found in 30% of the noses and sinuses of patients with CF. Other common bacteria found included Pseudomonas aeruginosa, coagulase negative staphylococci, and Haemophilus influenzae. In PCD, H. influenzae was the most common bacteria (28%), followed by Streptococcus pneumoniae and P. aeruginosa. If studies that included nonsurgical swab and blowing samples were excluded, then P. aeruginosa was the most common bacterium in patients with CF (34%) and in patients with PCD (50%), followed by S. aureus and H. influenza.ConclusionS. aureus, P. aeruginosa, coagulase negative staphylococci, and H. influenzae dominated in the upper airways of patients with CF. In patients with PCD, H. influenzae, S. pneumoniae, and P. aeruginosa dominated. When studies that included swab and blowing samples were excluded, P. aeruginosa was the most common bacterium in both groups. Direct comparisons among the studies were restricted due to very heterogeneous methods, and a better standardization of procedures and outcomes is needed.

Project description:BackgroundCystic Fibrosis (CF) is characterized by chronically inflamed airways, and inflammation even increases during pulmonary exacerbations. These adverse events have an important influence on the well-being, quality of life, and lung function of patients with CF. Prediction of exacerbations by inflammatory markers in exhaled breath condensate (EBC) combined with early treatment may prevent these pulmonary exacerbations and may improve the prognosis.AimTo investigate the diagnostic accuracy of a set of inflammatory markers in EBC to predict pulmonary exacerbations in children with CF.MethodsIn this one-year prospective observational study, 49 children with CF were included. During study visits with an interval of 2 months, a symptom questionnaire was completed, EBC was collected, and lung function measurements were performed. The acidity of EBC was measured directly after collection. Inflammatory markers interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), and macrophage migration inhibitory factor (MIF) were measured using high sensitivity bead based flow immunoassays. Pulmonary exacerbations were recorded during the study and were defined in two ways. The predictive power of inflammatory markers and the other covariates was assessed using conditionally specified models and a receiver operating characteristic curve (SAS version 9.2). In addition, k-nearest neighbors (KNN) algorithm was applied (SAS version 9.2).ResultsSixty-five percent of the children had one or more exacerbations during the study. The conditionally specified models showed an overall correct prediction rate of 55%. The area under the curve (AUC) was equal to 0.62. The results obtained with the KNN algorithm were very similar.ConclusionAlthough there is some evidence indicating that the predictors outperform random guessing, the general diagnostic accuracy of EBC acidity and the EBC inflammatory markers IL-6, IL-8, TNF-α and MIF is low. At present it is not possible to predict pulmonary exacerbations in children with CF with the chosen biomarkers and the method of EBC analysis. The biochemical measurements of EBC markers should be improved and other techniques should be considered.

Dataset Information

Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections.

Publications

Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections.

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