Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study

Abstract

Background: Early diagnosis of coronavirus disease 2019 (COVID-19) is of the utmost importance but remains challenging. The objective of the current study was to characterize exhaled breath from mechanically ventilated adults with COVID-19.

Methods: In this prospective observational study, we used real-time, online, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of expired air from adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS).

Findings: Between March 25^th and June 25^th, 2020, we included 40 patients with ARDS, of whom 28 had proven COVID-19. In a multivariate analysis, we identified a characteristic breathprint for COVID-19. We could differentiate between COVID-19 and non-COVID-19 ARDS with accuracy of 93% (sensitivity: 90%, specificity: 94%, area under the receiver operating characteristic curve: 0·94-0·98, after cross-validation). The four most prominent volatile compounds in COVID-19 patients were methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal.

Interpretation: The real-time, non-invasive detection of methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal in exhaled breath may identify ARDS patients with COVID-19.

Funding: The study was funded by Agence Nationale de la Recherche (SoftwAiR, ANR-18-CE45-0017 and RHU4 RECORDS, Programme d'Investissements d'Avenir, ANR-18-RHUS-0004), Région Île de France (SESAME 2016), and Fondation Foch.

Keywords: Breath analysis; COVID-19; Intensive care; Mechanical ventilation; Metabolomics.

Fig. 1

Multivariate analysis. Principal component analysis (left) and orthogonal partial least squares - discriminant analysis (right) of the breath signature in intubated, mechanically ventilated ICU patients with a positive (red) or negative (blue) PCR test for SARS-CoV-2.

Fig. 2

Receiver operating characteristic curves for models classifying patients with COVID-19 vs. non-COVID-19 ARDS. a. Complete model. The use of three machine learning algorithms (elastic net, support vector machine (SVM), and random forest (RF)) yielded an accuracy of up to 93%, with a 10-fold cross validation repeated four times and based on the selection of 19 features (elastic net), 16 features (random forest) or all 65 features (support vector machine) from the full dataset. After internal cross-validation, the sensitivity was 90% and the specificity was 94%. b. Model with the four most important features only. After internal cross-validation, the sensitivity ranged from 90% to 98% and the specificity ranged from 88% to 94%.

Fig. 3

Longitudinal analysis of VOCs in expired breath. The four features (m/z 99•08, 111•12, 135•09, and 143•15) contributing the most to the models were assessed in the first sample available for each patient (a) and over time (b) during the ICU stay for intubated, mechanically ventilated patients with COVID-19 ARDS (in red, n = 28) or non-COVID-19 ARDS (in blue, n = 12). All the points for a given patient are connected, and the bold lines correspond to the fixed effect of the mixed model for each group. p-values come from a Wilcoxon test (a) and an F-test (b).