Top

Published in:

Open Access 01-12-2024 | Acute Respiratory Distress-Syndrome | Research

Breath metabolomics for diagnosis of acute respiratory distress syndrome

Authors: Shiqi Zhang, Laura A. Hagens, Nanon F. L. Heijnen, Marry R. Smit, Paul Brinkman, Dominic Fenn, Tom van der Poll, Marcus J. Schultz, Dennis C. J. J. Bergmans, Ronny M. Schnabel, Lieuwe D. J. Bos, for the DARTS Consortium

Published in: Critical Care | Issue 1/2024

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.

Available only for authorised users

Meyer NJ, Gattinoni L, Calfee CS. Acute respiratory distress syndrome. Lancet. 2021;398(10300):622–37. https://doi.org/10.1016/S0140-6736(21)00439-6.CrossRefPubMedPubMedCentral

Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800. https://doi.org/10.1001/jama.2016.0291.CrossRefPubMed

Force ADT, Ranieri VM, Rubenfeld GD, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307(23):2526–33. https://doi.org/10.1001/jama.2012.5669.CrossRef

Allardet-Servent J, Forel JM, Roch A, et al. FIO₂ and acute respiratory distress syndrome definition during lung protective ventilation. Crit Care Med. 2009;37(1):202–7. https://doi.org/10.1097/CCM.0b013e31819261db.CrossRefPubMed

Determann RM, Royakkers AA, Haitsma JJ, Zhang H, Slutsky AS, Ranieri VM, Schultz MJ. Plasma levels of surfactant protein D and KL-6 for evaluation of lung injury in critically ill mechanically ventilated patients. BMC Pulm Med. 2010;10:6. https://doi.org/10.1186/1471-2466-10-6.CrossRefPubMedPubMedCentral

Kondo T, Hattori N, Ishikawa N, et al. KL-6 concentration in pulmonary epithelial lining fluid is a useful prognostic indicator in patients with acute respiratory distress syndrome. Respir Res. 2011;12:32. https://doi.org/10.1186/1465-9921-12-32.CrossRefPubMedPubMedCentral

Heijnen NFL, Hagens LA, Smit MR, et al. Biological subphenotypes of acute respiratory distress syndrome may not reflect differences in alveolar inflammation. Physiol Rep. 2021;9(3): e14693. https://doi.org/10.14814/phy2.14693.CrossRefPubMedPubMedCentral

Hagens LA, Heijnen NFL, Smit MR, Schultz MJ, Bergmans D, Schnabel RM, Bos LDJ. Systematic review of diagnostic methods for acute respiratory distress syndrome. ERJ Open Res. 2021. https://doi.org/10.1183/23120541.00504-2020.CrossRefPubMedPubMedCentral

Bos LD, Wang Y, Weda H, et al. A simple breath sampling method in intubated and mechanically ventilated critically ill patients. Respir Physiol Neurobiol. 2014;191:67–74. https://doi.org/10.1016/j.resp.2013.11.001.CrossRefPubMed

10.

Żuchowska K, Filipiak W. Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria: current state of the art. J Pharmac Anal. 2023. https://doi.org/10.1016/j.jpha.2023.11.005.CrossRef

11.

Munoz-Lucas MA, Wagner-Struwing C, Jareno-Esteban J, et al. Differences in volatile organic compounds (VOC) determined in exhaled breath in two populations of lung cancer (LC): with and without COPD. Eur Respir J. 2013;42(Suppl 57):P2892.

12.

Ibrahim W, Cordell RL, Wilde MJ, et al. Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography-mass spectrometry. ERJ Open Res. 2021. https://doi.org/10.1183/23120541.00139-2021.CrossRefPubMedPubMedCentral

13.

Heijnen NFL, Hagens LA, van Schooten FJ, et al. Breath octane and acetaldehyde as markers for acute respiratory distress syndrome in invasively ventilated patients suspected to have ventilator-associated pneumonia. ERJ Open Res. 2022. https://doi.org/10.1183/23120541.00624-2021.CrossRefPubMedPubMedCentral

14.

Hagens LA, Verschueren ARM, Lammers A, et al. Development and validation of a point-of-care breath test for octane detection. Analyst. 2021;146(14):4605–14. https://doi.org/10.1039/d1an00378j.CrossRefPubMed

15.

Bos LD, Weda H, Wang Y, et al. Exhaled breath metabolomics as a noninvasive diagnostic tool for acute respiratory distress syndrome. Eur Respir J. 2014;44(1):188–97. https://doi.org/10.1183/09031936.00005614.CrossRefPubMed

16.

Hagens LA, Heijnen NFL, Smit MR, et al. Octane in exhaled breath to diagnose acute respiratory distress syndrome in invasively ventilated intensive care unit patients. ERJ Open Res. 2023. https://doi.org/10.1183/23120541.00214-2023.CrossRefPubMedPubMedCentral

17.

Hagens LA, Heijnen NFL, Smit MR, et al. Diagnosis of acute respiratory distress syndrome (DARTS) by bedside exhaled breath octane measurements in invasively ventilated patients: protocol of a multicentre observational cohort study. Ann Transl Med. 2021;9(15):1262. https://doi.org/10.21037/atm-21-1384.CrossRefPubMedPubMedCentral

18.

Hagens LA, Van der Ven F, Heijnen NFL, et al. Improvement of an interobserver agreement of ARDS diagnosis by adding additional imaging and a confidence scale. Front Med. 2022;9: 950827. https://doi.org/10.3389/fmed.2022.950827.CrossRef

19.

van Oort PMP, White IR, Ahmed W, et al. Detection and quantification of exhaled volatile organic compounds in mechanically ventilated patients—comparison of two sampling methods. Analyst. 2021;146(1):222–31. https://doi.org/10.1039/c9an01134j.CrossRefPubMed

20.

Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:441.CrossRef

21.

Pate A, Riley RD, Collins GS, van Smeden M, Van Calster B, Ensor J, Martin GP. Minimum sample size for developing a multivariable prediction model using multinomial logistic regression. Stat Methods Med Res. 2023;32(3):555–71. https://doi.org/10.1177/09622802231151220.CrossRefPubMedPubMedCentral

22.

Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.CrossRef

23.

Garge NR, Bobashev G, Eggleston B. Random forest methodology for model-based recursive partitioning: the mobForest package for R. BMC Bioinformatics. 2013;14:125. https://doi.org/10.1186/1471-2105-14-125.CrossRefPubMedPubMedCentral

24.

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.CrossRefPubMedPubMedCentral

25.

Bos LD, Schultz MJ, Sterk PJ. Exhaled breath profiling for diagnosing acute respiratory distress syndrome. BMC Pulm Med. 2014;14:72. https://doi.org/10.1186/1471-2466-14-72.CrossRefPubMedPubMedCentral

26.

Zhou M, Sharma R, Zhu H, et al. Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device. Anal Bioanal Chem. 2019;411(24):6435–47. https://doi.org/10.1007/s00216-019-02024-5.CrossRefPubMedPubMedCentral

27.

Ware LB, Koyama T, Zhao Z, et al. Biomarkers of lung epithelial injury and inflammation distinguish severe sepsis patients with acute respiratory distress syndrome. Crit Care. 2013;17(5):R253. https://doi.org/10.1186/cc13080.CrossRefPubMedPubMedCentral

28.

Leblanc D, Bouvet C, Degiovanni F, et al. Early lung ultrasonography predicts the occurrence of acute respiratory distress syndrome in blunt trauma patients. Intensive Care Med. 2014;40(10):1468–74. https://doi.org/10.1007/s00134-014-3382-9.CrossRefPubMed

29.

Villar J, Herran-Monge R, Gonzalez-Higueras E, et al. Clinical and biological markers for predicting ARDS and outcome in septic patients. Sci Rep. 2021;11(1):22702. https://doi.org/10.1038/s41598-021-02100-w.CrossRefPubMedPubMedCentral

30.

Broeckaert F, Bernard A. Clara cell secretory protein (CC16): characteristics and perspectives as lung peripheral biomarker. Clin Exp Allergy. 2000;30(4):469–75. https://doi.org/10.1046/j.1365-2222.2000.00760.x.CrossRefPubMed

31.

Lin J, Zhang W, Wang L, Tian F. Diagnostic and prognostic values of club cell protein 16 (CC16) in critical care patients with acute respiratory distress syndrome. J Clin Lab Anal. 2018. https://doi.org/10.1002/jcla.22262.CrossRefPubMedPubMedCentral

32.

Bos LD, Sterk PJ, Schultz MJ. Volatile metabolites of pathogens: a systematic review. PLoS Pathog. 2013;9(5): e1003311. https://doi.org/10.1371/journal.ppat.1003311.CrossRefPubMedPubMedCentral

33.

Sunesson A, Vaes W, Nilsson C, Blomquist G, Andersson B, Carlson R. Identification of volatile metabolites from five fungal species cultivated on two media. Appl Environ Microbiol. 1995;61(8):2911–8. https://doi.org/10.1128/aem.61.8.2911-2918.1995.CrossRefPubMedPubMedCentral

34.

Borjesson T, Stollman U, Schnurer J. Volatile metabolites produced by six fungal species compared with other indicators of fungal growth on cereal grains. Appl Environ Microbiol. 1992;58(8):2599–605. https://doi.org/10.1128/aem.58.8.2599-2605.1992.CrossRefPubMedPubMedCentral

35.

Micheluz A, Manente S, Rovea M, Slanzi D, Varese GC, Ravagnan G, Formenton G. Detection of volatile metabolites of moulds isolated from a contaminated library. J Microbiol Methods. 2016;128:34–41. https://doi.org/10.1016/j.mimet.2016.07.004.CrossRefPubMed

36.

Trefz P, Koehler H, Klepik K, Moebius P, Reinhold P, Schubert JK, Miekisch W. Volatile emissions from Mycobacterium avium subsp. paratuberculosis mirror bacterial growth and enable distinction of different strains. PLoS ONE. 2013;8(10):e76868. https://doi.org/10.1371/journal.pone.0076868.CrossRefPubMedPubMedCentral

37.

Barberis E, Amede E, Khoso S, et al. Metabolomics diagnosis of COVID-19 from exhaled breath condensate. Metabolites. 2021. https://doi.org/10.3390/metabo11120847.CrossRefPubMedPubMedCentral

38.

Berna AZ, Akaho EH, Harris RM, et al. Reproducible breath metabolite changes in children with SARS-CoV-2 infection. ACS Infect Dis. 2021;7(9):2596–603. https://doi.org/10.1021/acsinfecdis.1c00248.CrossRefPubMedPubMedCentral

39.

Whiteson KL, Meinardi S, Lim YW, et al. Breath gas metabolites and bacterial metagenomes from cystic fibrosis airways indicate active pH neutral 2,3-butanedione fermentation. ISME J. 2014;8(6):1247–58. https://doi.org/10.1038/ismej.2013.229.CrossRefPubMedPubMedCentral

40.

Bos LD, Meinardi S, Blake D, Whiteson K. Bacteria in the airways of patients with cystic fibrosis are genetically capable of producing VOCs in breath. J Breath Res. 2016;10(4):047103. https://doi.org/10.1088/1752-7163/10/4/047103.CrossRefPubMed

41.

Sanchez JM, Sacks RD. Development of a multibed sorption trap, comprehensive two-dimensional gas chromatography, and time-of-flight mass spectrometry system for the analysis of volatile organic compounds in human breath. Anal Chem. 2006;78(9):3046–54. https://doi.org/10.1021/ac060053k.CrossRefPubMed

42.

Van Berkel JJ, Dallinga JW, Moller GM, Godschalk RW, Moonen E, Wouters EF, Van Schooten FJ. Development of accurate classification method based on the analysis of volatile organic compounds from human exhaled air. J Chromatogr B Analyt Technol Biomed Life Sci. 2008;861(1):101–7. https://doi.org/10.1016/j.jchromb.2007.11.008.CrossRefPubMed

43.

Nathani N, Perkins GD, Tunnicliffe W, Murphy N, Manji M, Thickett DR. Kerbs von Lungren 6 antigen is a marker of alveolar inflammation but not of infection in patients with acute respiratory distress syndrome. Crit Care. 2008;12(1):R12. https://doi.org/10.1186/cc6785.CrossRefPubMedPubMedCentral

44.

Belizario JE, Faintuch J, Malpartida MG. Breath biopsy and discovery of exclusive volatile organic compounds for diagnosis of infectious diseases. Front Cell Infect Microbiol. 2020;10: 564194. https://doi.org/10.3389/fcimb.2020.564194.CrossRefPubMed

Title: Breath metabolomics for diagnosis of acute respiratory distress syndrome
Authors: Shiqi Zhang
Laura A. Hagens
Nanon F. L. Heijnen
Marry R. Smit
Paul Brinkman
Dominic Fenn
Tom van der Poll
Marcus J. Schultz
Dennis C. J. J. Bergmans
Ronny M. Schnabel
Lieuwe D. J. Bos
for the DARTS Consortium
Publication date: 01-12-2024
Publisher: BioMed Central
Keyword: Acute Respiratory Distress-Syndrome
Published in: Critical Care / Issue 1/2024
Electronic ISSN: 1364-8535
DOI: https://doi.org/10.1186/s13054-024-04882-7

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Breath metabolomics for diagnosis of acute respiratory distress syndrome

Abstract

Background

Aim

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Aim

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2024

Cardiogenic shock: all hail the RCT, long live the registry

Extracorporeal cardiopulmonary resuscitation versus conventional CPR in cardiac arrest: be aware of the temporal selection bias

Trauma systems in Asian countries: challenges and recommendations

Gut mycobiome dysbiosis after sepsis and trauma

Triggering receptor expressed on myeloid cells-1 in sepsis, and current insights into clinical studies

Association between intensive care unit nursing grade and mortality in patients with cardiogenic shock and its cost-effectiveness