Predicting steroid responsiveness in patients with asthma using exhaled breath profiling.

BACKGROUND

Exhaled breath contains disease-dependent volatile organic compounds (VOCs), which may serve as biomarkers distinguishing clinical phenotypes in asthma. Their measurement may be particularly beneficial in relation to treatment response.

OBJECTIVE

Our aim was to compare the performance of electronic nose (eNose) breath analysis with previously investigated techniques (sputum eosinophils, exhaled nitric oxide (FeNO) and airway hyperresponsiveness) to discriminate asthma from controls and identify steroid responsiveness in steroid-free patients. Trial registration ACTRN12613000038796.

METHODS

Twenty-five patients with mild/moderate asthma had their inhaled steroid treatment discontinued until loss of control or 28 days. They were subsequently treated with oral prednisone 30 mg/day for 14 days. Steroid responsiveness was defined as an increase of either > 12% FEV1 or > 2 doubling doses PC20 AMP. Steroid-free assessment of sputum eosinophils, FeNO and exhaled breath VOCs were used to construct algorithms predicting steroid responsiveness. Performance characteristics were compared by ROC analysis.

RESULTS

The eNose discriminated between asthma and controls (area under the curve = 0.766 ± 0.14; P = 0.002) with similar accuracy to FeNO (0.862 ± 0.12; P < 0.001) and sputum eosinophils (0.814 ± 0.15; P < 0.001). Steroid responsiveness was predicted with greater accuracy by VOC-analysis (AUC = 0.883 ± 0.16; P = 0.008) than FeNO (0.545 ± 0.28; P = 0.751) or sputum eosinophils (0.610 ± 0.29; P = 0.441).

CONCLUSIONS AND CLINICAL RELEVANCE

Breath analysis by eNose can identify asthmatic patients and may be used to predict their response to steroids with greater accuracy than sputum eosinophils or FeNO. This implies a potential role for breath analysis in the tailoring of treatment for asthma patients.