Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device
Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients’ breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes.
KeywordsBreath analysis Acute respiratory distress syndrome gas chromatography 2D GC Machine learning
They thank the support from Flux HPC Cluster provided by the University of Michigan Office of Research and Advanced Research Computing–Technology Services (ARC-TS), and Analytical Chemistry Lab at the University of Michigan Biological Station at Pellston, Michigan, for using their mass spectrometry facility.
The authors received financial support from National Institutes of Health (1-R21-HL-139156-01), NIH Center for Accelerated Innovations at Cleveland Clinic (Program Prime Award Number: 1UH54HL119810-05; Project Number: NCAI-17-7-APP-UMICH-Fan), the Michigan Translation and Commercialization (MTRAC) for Life Sciences Hub, and the Michigan Center for Integrative Research in Critical Care.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Research involving human participants and/or animals
Research involves human participants. The clinical research study (HUM00103401) was approved by the University of Michigan’s Human Research Protection Program.
Consent was obtained from patient subjects or their legally authorized representatives by means of a voluntarily completed consent form prior to enrollment.
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