TY - JOUR
T1 - Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation
AU - Chen, Dapeng
AU - Mirski, Marek A.
AU - Chen, Shuo
AU - Devin, Alese P.
AU - Haddaway, Caroline R.
AU - Caton, Emily R.
AU - Bryden, Wayne A.
AU - McLoughlin, Michael
N1 - Funding Information:
We thank Rebecca Z Fenselau (Zeteo Tech, Inc.) for editing the manuscript. The study was supported by the internal reseach & development funding by Zeteo Tech, Inc.
Publisher Copyright:
© 2023 IOP Publishing Ltd.
PY - 2023/4
Y1 - 2023/4
N2 - Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with the p-values of 2.0 × 10−5, 1.1 × 10−4, and 1.7 × 10−3, respectively, using multiple logistic regression. Furthermore, a score system named ‘TrunScore’ was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.
AB - Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with the p-values of 2.0 × 10−5, 1.1 × 10−4, and 1.7 × 10−3, respectively, using multiple logistic regression. Furthermore, a score system named ‘TrunScore’ was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.
KW - critical care
KW - human exhaled air
KW - noninvasive diagnosis
KW - respiratory tract infections
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U2 - 10.1088/1752-7163/acad92
DO - 10.1088/1752-7163/acad92
M3 - Article
C2 - 36542858
AN - SCOPUS:85146532229
SN - 1752-7155
VL - 17
JO - Journal of breath research
JF - Journal of breath research
IS - 2
M1 - 026001
ER -