Exhaled Breath Condensate in Childhood Asthma: A Review and Current Perspective

Exhaled breath condensate (EBC) was introduced more than two decades ago as a novel, non-invasive tool to assess airway inflammation. This review summarizes the latest literature on the various markers in EBC to predict asthma in children. Despite many recommendations and two comprehensive Task Force reports, there is still large heterogeneity in published data. The biggest issue remains a lack of standardization regarding EBC collection, preservation, processing, and analysis. As a result, published studies show mixed or conflicting results, questioning the reproducibility of findings. A joint, multicenter research study is urgently needed to address the necessary methodological standardization

Clinical presentation of Churg-Strauss syndrome in children.

Churg-Strauss syndrome is an uncommon multisystem disorder characterized by asthma, eosinophilia and vasculitis. We report on a 12-year-old boy with asthma and deterioration of his general condition, who was eventually diagnosed with an ANCA-negative Churg-Strauss syndrome. The propositus included, 50 cases of childhood Churg-Strauss syndrome have been reported. The patient characteristics and clinical characteristics of these children are summarized. The respiratory tract is most frequently involved with pulmonary infiltrates, asthma and sinusitis. Early recognition of childhood Churg-Strauss syndrome is important as delayed diagnosis can lead to severe organ involvement, and possible fatal outcome

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar

Can exhaled volatile organic compounds predict asthma exacerbations in children?

Background. Asthma control does not yet meet the goals of asthma management guidelines. Noninvasive monitoring of airway inflammation may help to improve the level of asthma control in children. Objectives. (1) To identify a set of exhaled volatile organic compounds (VOCs) that is most predictive for an asthma exacerbation in children. (2) To elucidate the chemical identity of predictive biomarkers. Methods. In a one-year prospective observational study, 96 asthmatic children participated. During clinical visits at 2 month intervals, asthma control, fractional exhaled nitric oxide, lung function (FEV1, FEV1/VC) and VOCs in exhaled breath were determined by means of gas chromatography time-of-flight mass spectrometry. Random Forrest classification modeling was used to select predictive VOCs, followed by plotting of receiver operating characteristic-curves (ROCcurves). Results. An inverse relationship was found between the predictive power of a set of VOCs and the time between sampling of exhaled breath and the onset of exacerbation. The sensitivity and specificity of the model predicting exacerbations 14 days after sampling were 88% and 75%, respectively. The area under the ROC-curve was 90%. The sensitivity for prediction of asthma exacerbations within 21 days after sampling was 63%. In total, 7 VOCs were selected for the classification model: 3 aldehydes, 1 hydrocarbon, 1 ketone, 1 aromatic compound, and 1 unidentified VOC. Conclusion. VOCs in exhaled breath showed potential for predicting asthma exacerbations in children within 14 days after sampling. Before using this in clinical practice, the validity of predicting asthma exacerbations should be studied in a larger cohort.</p

Overview of ROC-curves of 3 predictive models for asthma exacerbations.

<p>ROC1: exacerbation prediction model on the basis of the acidity of EBC and inflammatory markers in EBC alone; ROC2: model on the basis of FeNO, reversibility to a bronchodilator as increase in FEV1% of predicted value, PC₂₀, daily dosage of ICS; ROC3: model all variables of model 1 and 2.</p

Overview of study parameters.

<p>ACQ = Asthma Control Questionnaire, GINA = Global Initiative for Asthma, FeNO = Fractional exhaled Nitric Oxide, EBC = exhaled breath condensate, PC₂₀ = histamine bronchial hyperresponsiveness test.</p><p>* Symptom score based on GINA criteria was collected during 2 weeks preceding the clinical visit. This score was combined with FEV₁, to assess asthma control as defined by GINA.</p><p>^† Home monitoring consisted of daily symptom score plus FEV₁ measurements.</p><p>Overview of study parameters.</p

KNN- prediction of asthma exacerbation based on acidity of EBC, inflammatory markers in EBC, FeNO, and asthma clinical characteristics.^*

<p>* KNN algorithm is performed as statistical technique.</p><p>KNN- prediction of asthma exacerbation based on acidity of EBC, inflammatory markers in EBC, FeNO, and asthma clinical characteristics.^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0119434#t005fn001" target="_blank">*</a>}</p