Pneumoconiosis is a traditional occupational disease and has reemerged in recent years. Current medical surveillance program have flaws that may result in poor detection of early pneumoconiosis around the world. Pneumoconiosis could generates 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 using a senor array technique. We conducted a case-control study that enrolled 36 asymptomatic cases of pneumoconiosis and 64 healthy controls between October and November 2016 to construct the prediction model. 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. We used the profusion category&x2267;1/1 in chest X-ray in accordance with the ILO-2011D criteria as the reference standard to assess the diagnostic accuracy. Data were randomly split into 80% for model building and 20% for validation. By linear discriminant analysis, the sensitivity was 71.0%, specificity was 91.8%, accuracy was 86.8%, and ROC-AUC was 0.89 in the training set, and the sensitivity was 80.0%, specificity was 66.7%, accuracy was 70.0%, and ROC-AUC was 0.79 in the validation set. Breath test might have potential in the screening for pneumoconiosis; however, a multi-centre study is warranted to establish a reliable model and all procedures must be standardised before clinical application.
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