Detection of Pseudomonas aeruginosa in sputum headspace through volatile organic compound analysis

Chronic pulmonary infection is the hallmark of cystic fibrosis lung disease. Searching for faster and easier screening may lead to faster diagnosis and treatment of Pseudomonas aeruginosa (P. aeruginosa). Our aim was to analyze and build a model to predict the presence of P. aeruginosa in sputa.

Sputa from 28 bronchiectatic patients were used for bacterial culturing and analysis of volatile compounds by gas chromatography-mass spectrometry. Data analysis and model building were done by Partial Least Squares Regression Discriminant analysis (PLS-DA). Two analysis were performed: one comparing P. aeruginosa positive with negative cultures at study visit (PA model) and one comparing chronic colonization according to the Leeds criteria with P. aeruginosa negative patients (PACC model).

The PA model prediction of P. aeruginosa presence was rather poor, with a high number of false positives and false negatives. On the other hand, the PACC model was stable and explained chronic P. aeruginosa presence for 95% with 4 PLS-DA factors, with a sensitivity of 100%, a positive predictive value of 86% and a negative predictive value of 100%.

Our study shows the potential for building a prediction model for the presence of chronic P. aeruginosa based on volatiles from sputum.