690 Breath test for pneumoconiosis using an electronic nose: a case-control study

    loading  Checking for direct PDF access through Ovid

Abstract

Introduction

Current medical surveillance program has flaw that may result in poor detection of early pneumoconiosis around the world. Breath analyses have attracted substantial attention in the screening of occupational environmental lung disease. Pneumoconiosis could generate specific volatile organic compounds (VOCs) that may constitute a specific breath print for diagnosis. The objective of this study was to develop a breath test for pneumoconiosis by analysing VOCs using senor array technique.

Methods

We conducted a case-control study and enrolled study subjects from stone workers in Hualien between October 2016 and November 2016. One litter of breath air was collected after five minutes of tidal breathing through a non-rebreathing valve with inspiratory VOC-filter, and storage by a Tedlar bag. The air was analysed by a 32 nanocomposite sensor array electronic nose within 30 min. Using the ILO/ICRP profusion category ≥1/1 in chest X-ray as the reference standard, we assessed the diagnostic accuracy of the breath test. Data were randomly split into 80% for model building and 20% for validation.

Result

After excluding three subjects with poorly controlled diabetes mellitus with fasting serum glucose level >200 mg/dl and one subjects of asthma under medication, a total of 98 subjected were used in final analysis that included 34 cases of pneumoconiosis and 64 healthy controls. By linear discriminant analysis, the sensitivity was 88.0%, specificity was 67.9%, accuracy was 80.8%, and ROC-AUC was 0.91 (95% CI: 0.85 to 0.97) in the training set. In the validation set, the sensitivity was 66.7%, specificity was 71.4%, accuracy was 70.0%, and ROC-AUC was 0.86 (95% CI: 0.69 to 1.00).

Discussion

Breath test may have potential in screening for pneumoconiosis. A multi-centre study is warranted to establish a reliable model and the procedures must be standardised to prevent confounding factors before clinical application.

Related Topics

    loading  Loading Related Articles