Breath metabolomics for diagnosis of acute respiratory distress syndrome

Abstract

Background: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.

Aim: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.

Methods: This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC-MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS).

Results: A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63-0.78) in the derivation cohort and 0.63 (CI 0.52-0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance.

Conclusion: An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Keywords: ARDS; Breath analysis; Prediction model; VOCs.

Fig. 1

Flowchart of screening and inclusion process. MV Mechanical ventilation, GCMS Gas chromatography-mass spectrometry, ARDS Acute respiratory distress syndrome

Fig. 2

Receiver operating characteristics (ROC) curve per metabolite that were included in the VOC-ARDS score classifier and the combined VOC-ARDS classifier with the lung injury prediction score (LIPS) for each cohort. A–B in the derivation cohort. C–D in the validation cohort. E–F: in all the included patients. Mey 1-methylpyrrole, Tri 1,3,5-trifluorobenzene, Mea Methoxyacetic acid, Fur 2-methylfuran, Mep 2-methyl-1-propanol, AUC Area under curve, VOCs Volatile organic compounds, LIPS Lung injury prediction score

Fig. 3

The association between exhaled breath VOCs and plasma biomarker levels. Every correlation coefficient between two variables was calculated using spearman correlation, color depth of each box indicates the correlation strength. Mey 1-methylpyrrole, Tri 1,3,5-trifluorobenzene, Mea Methoxyacetic acid, Fur 2-methylfuran, Mep 2-methyl-1-propanol. Ang-1 Angiopoietin-1, Ang-2 Angiopoietin-2, Ang2/1 The ratio of Angiopoietin-1/ Angiopoietin-2, CC-16 = Club (Clara) cell protein 16, VEGF Vascular endothelial growth factor, GM-CSF Granulocyte–macrophage colony-stimulating factor, ICAM Intercellular adhesion molecule, IFN Interferon, IL Interleukin, KL-6 Krebs von den Lungen-6, MMP-8 Matrix metalloproteinase-8, PAI-1 Plasminogen activator inhibitor-1, RAGE Receptor for advanced glycation end-products, SP-D Surfactant protein-D, TNF Tumor-necrosis factor, TNF RI Tumor-necrosis factor receptor 1, VCAM Vascular adhesion molecule, VEGF Vascular endothelial growth factor, vWF Von Willebrand Factor