Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable 2-dimensional gas chromatography device

Abstract

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, high sensitivity, and rapid analysis capability 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 minutes. 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.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

“…The preprint of this work that contained the preliminary results was published at bioRxiv [46], which can be accessed at https://www.biorxiv.org/content/10.1101/560888v1.…”

Breath analysis for detection and trajectory monitoring of acute respiratory distress syndrome in swine

“…The corresponding statistics is summarized in Table 2. The data demonstrate that pre-ARDS and ARDS are well separated and that the distribution of the pre-ARDS data points is much more clustered than that for the non-ARDS human subjects in our previous study [13], perhaps indicative of the homogeneity of the swine model in comparison with the human subjects. Figures S2 and S3 show the PCA scores for the testing set and for all the data sets, respectively.…”

“…The raw chromatograms were first pre-processed for noise reduction, curve smoothing, alignment with the reference chromatogram, and peak assignment. After pre-processing, the area of each peak was calculated and normalized by the entire area under the chromatogram curve [13,25]. Through machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) [13,17,18], a sub-set of chromatographic peaks were selected as the biomarkers for the discrimination of ARDS.…”

“…After pre-processing, the area of each peak was calculated and normalized by the entire area under the chromatogram curve [13,25]. Through machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) [13,17,18], a sub-set of chromatographic peaks were selected as the biomarkers for the discrimination of ARDS. The details about the GC system and the algorithm used in data analysis can be found in [13,[25][26][27].…”

“…Therefore, breath analysis can potentially be very useful for non-invasive ARDS detection and continuous monitoring [2,3,[11][12][13][14]. In a previous study, we predicted ARDS in human subjects with high accuracy when compared to clinical adjudication based on Berlin criteria [13]. The study was limited by the absence of confirmatory, histopathologic evidence of ARDS (i.e., diffuse alveolar damage).…”