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Long GA, Xu Q, Sunkara J, Woodbury R, Brown K, Huang JJ, Xie Z, Chen X, Fu XA, Huang J. A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds. Diagn Microbiol Infect Dis 2024; 109:116309. [PMID: 38692202 DOI: 10.1016/j.diagmicrobio.2024.116309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024]
Abstract
BACKGROUND The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection. METHODS This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted. RESULTS The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS. CONCLUSION Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.
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Affiliation(s)
- Grace A Long
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Qian Xu
- Biometrics and Data Science, Fosun Pharma, Beijing, PR China
| | - Jahnavi Sunkara
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Reagan Woodbury
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Katherine Brown
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | | | - Zhenzhen Xie
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - Xiaoyu Chen
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.
| | - Xiao-An Fu
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA.
| | - Jiapeng Huang
- Department of Anesthesiology & Perioperative Medicine, University of Louisville, Louisville, KY, USA..
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2
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Xie Z, Morris JD, Pan J, Cooke EA, Sutaria SR, Balcom D, Marimuthu S, Parrish LW, Aliesky H, Huang JJ, Rai SN, Arnold FW, Huang J, Nantz MH, Fu XA. Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath. Sci Rep 2024; 14:14568. [PMID: 38914586 PMCID: PMC11196736 DOI: 10.1038/s41598-024-61735-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 05/09/2024] [Indexed: 06/26/2024] Open
Abstract
COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.
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Affiliation(s)
- Zhenzhen Xie
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - James D Morris
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - Jianmin Pan
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- The Cancer Data Science Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Biostatistics and Informatics Shared Resource, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | - Elizabeth A Cooke
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA
| | - Saurin R Sutaria
- Department of Chemistry, University of Louisville, Louisville, KY, USA
| | - Dawn Balcom
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Subathra Marimuthu
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Leslie W Parrish
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Holly Aliesky
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | | | - Shesh N Rai
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- The Cancer Data Science Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Biostatistics and Informatics Shared Resource, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | - Forest W Arnold
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA.
| | - Michael H Nantz
- Department of Chemistry, University of Louisville, Louisville, KY, USA.
| | - Xiao-An Fu
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA.
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Padawer D, Qadan A, Karameh M, Darawshy F, Laor A, Banker S, Fridlender ZG. Breath of Health: spectroscopy-based breath test for the detection of SARS-CoV-2. Infect Dis (Lond) 2024; 56:376-383. [PMID: 38424673 DOI: 10.1080/23744235.2024.2313020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 01/27/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Nucleic acid amplification tests (NAAT) are considered the gold standard for COVID-19 diagnosis. These tests require professional manpower and equipment, long processing and swab sampling which is unpleasant to the patients. Several volatile organic compounds (VOCs) have been identified in the breath of COVID-19 patients. Detection of these VOCs using a breath test could help rapidly identify COVID-19 patients. OBJECTIVE Assess the accuracy of 'Breath of Health' (BOH) COVID-19 Fourier-transform infra-red (FTIR) Spectroscopy-based breath test. METHODS Breath samples from patients with or without symptoms suggestive for COVID-19 who had NAAT results were collected using Tedlar bags and were blindly analysed using BOH FTIR spectroscopy. BOH Measures several VOCs simultaneously and differentiating positive and negative results. BOH results were compared to NAAT results as gold standard. RESULTS Breath samples from 531 patients were analysed. The sensitivity of BOH breath test was found to be 79.5% and specificity was 87.2%. Positive predictive value (PPV) was 74.7% and negative predictive value (NPV) 90.0%. Calculated accuracy rate was 84.8% and area under the curve 0.834. Subgroup analysis revealed that the NPV of patients without respiratory symptoms was superior over the NPV of symptomatic patients (94.7% vs 80.7%, P-value < 0.0001) and PPV of patients with respiratory symptoms outranks the PPV of individuals without symptoms (85.3% vs 69.2%, P-value 0.0196). CONCLUSION We found BOH COVID-19 breath test to be a patient-friendly, rapid, non-invasive diagnostic test with high accuracy rate and NPV that could efficiently rule out COVID-19 especially among individuals with low pre-test probability.
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Affiliation(s)
- Dan Padawer
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Abed Qadan
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mutaz Karameh
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
- Heart Institute, Hadassah Medical Center, Jerusalem, Israel
| | - Fares Darawshy
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Arie Laor
- Breath of Health Ltd, Rehovot, Israel
| | | | - Zvi G Fridlender
- Institute of Pulmonary Medicine, Hadassah Medical Center, Jerusalem, Israel
- Department of Internal Medicine D, Hadassah Medical Center, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
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4
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Cusack RP, Larracy R, Morrell CB, Ranjbar M, Le Roux J, Whetstone CE, Boudreau M, Poitras PF, Srinathan T, Cheng E, Howie K, Obminski C, O'Shea T, Kruisselbrink RJ, Ho T, Scheme E, Graham S, Beydaghyan G, Gavreau GM, Duong M. Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study. J Breath Res 2024; 18:026009. [PMID: 38382095 DOI: 10.1088/1752-7163/ad2b6e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.
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Affiliation(s)
- Ruth P Cusack
- Department of Respiratory Medicine, Galway University Hospital, Galway, Ireland
- School of Medicine, University of Galway, Galway, Ireland
| | - Robyn Larracy
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Christian B Morrell
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Maral Ranjbar
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jennifer Le Roux
- Department of Medicine, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada
| | | | | | | | - Thiviya Srinathan
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, L8N 4A6, Canada
| | - Eric Cheng
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, L8N 4A6, Canada
| | - Karen Howie
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Catie Obminski
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tim O'Shea
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | | | - Terence Ho
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Erik Scheme
- Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | | | | | - Gail M Gavreau
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - MyLinh Duong
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Firestone Institute for Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, L8N 4A6, Canada
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Sola-Martínez RA, Zeng J, Awchi M, Gisler A, Arnold K, Singh KD, Frey U, Díaz MC, de Diego Puente T, Sinues P. Preservation of exhaled breath samples for analysis by off-line SESI-HRMS: proof-of-concept study. J Breath Res 2023; 18:011002. [PMID: 38029449 DOI: 10.1088/1752-7163/ad10e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/29/2023] [Indexed: 12/01/2023]
Abstract
Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) is an established technique in the field of breath analysis characterized by its short analysis time, as well as high levels of sensitivity and selectivity. Traditionally, SESI-HRMS has been used for real-time breath analysis, which requires subjects to be at the location of the analytical platform. Therefore, it limits the possibilities for an introduction of this methodology in day-to-day clinical practice. However, recent methodological developments have shown feasibility on the remote sampling of exhaled breath in Nalophan® bags prior to measurement using SESI-HRMS. To further explore the range of applications of this method, we conducted a proof-of-concept study to assess the impact of the storage time of exhaled breath in Nalophan® bags at different temperatures (room temperature and dry ice) on the relative intensities of the compounds. In addition, we performed a detailed study of the storage effect of 27 aldehydes related to oxidative stress. After 2 h of storage, the mean of intensity of allm/zsignals relative to the samples analyzed without prior storage remained above 80% at both room temperature and dry ice. For the 27 aldehydes, the mean relative intensity losses were lower than 20% at 24 h of storage, remaining practically stable since the first hour of storage following sample collection. Furthermore, the mean relative intensity of most aldehydes in samples stored at room temperature was higher than those stored in dry ice, which could be related to water vapor condensation issues. These findings indicate that the exhaled breath samples could be preserved for hours with a low percentage of mean relative intensity loss, thereby allowing more flexibility in the logistics of off-line SESI-HRMS studies.
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Affiliation(s)
- Rosa A Sola-Martínez
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
| | - Jiafa Zeng
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Mo Awchi
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Amanda Gisler
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
| | - Kim Arnold
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Kapil Dev Singh
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Urs Frey
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Manuel Cánovas Díaz
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
| | - Teresa de Diego Puente
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
| | - Pablo Sinues
- University of Basel Children's Hospital (UKBB), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
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Sutaria SR, Morris JD, Xie Z, Cooke EA, Silvers SM, Long GA, Balcom D, Marimuthu S, Parrish LW, Aliesky H, Arnold FW, Huang J, Fu XA, Nantz MH. A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19. J Breath Res 2023; 18:016004. [PMID: 37875100 PMCID: PMC10620812 DOI: 10.1088/1752-7163/ad0646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/14/2023] [Accepted: 10/24/2023] [Indexed: 10/26/2023]
Abstract
A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.
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Affiliation(s)
- Saurin R Sutaria
- Departments of Chemistry, University of Louisville, Louisville, KY 40292, United States of America
| | - James D Morris
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Zhenzhen Xie
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Elizabeth A Cooke
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Shavonne M Silvers
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Grace A Long
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Dawn Balcom
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Subathra Marimuthu
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Leslie W Parrish
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Holly Aliesky
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Forest W Arnold
- Division of Infectious Diseases, Department of Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Jiapeng Huang
- Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY 40292, United States of America
| | - Xiao-An Fu
- Chemical Engineering, University of Louisville, Louisville, KY 40292, United States of America
| | - Michael H Nantz
- Departments of Chemistry, University of Louisville, Louisville, KY 40292, United States of America
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7
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Awchi M, Singh KD, Dill PE, Frey U, Datta AN, Sinues P. Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents. J Breath Res 2023; 17:046013. [PMID: 37678210 DOI: 10.1088/1752-7163/acf782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 09/07/2023] [Indexed: 09/09/2023]
Abstract
Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.
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Affiliation(s)
- Mo Awchi
- University Children's Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Kapil Dev Singh
- University Children's Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | | | - Urs Frey
- University Children's Hospital Basel, Basel, Switzerland
| | | | - Pablo Sinues
- University Children's Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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8
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Borras E, McCartney MM, Rojas DE, Hicks TL, Tran NK, Tham T, Juarez MM, Franzi L, Harper RW, Davis CE, Kenyon NJ. Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients. J Breath Res 2023; 17:10.1088/1752-7163/acea3d. [PMID: 37489864 PMCID: PMC10446499 DOI: 10.1088/1752-7163/acea3d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.
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Affiliation(s)
- Eva Borras
- Mechanical and Aerospace Engineering, One Shields Avenue, University of California, Davis, Davis, California, USA
- UC Davis Lung Center, University of California Davis, CA
- These authors contributed equally: Eva Borras, Mitchell M. McCartney
| | - Mitchell M. McCartney
- Mechanical and Aerospace Engineering, One Shields Avenue, University of California, Davis, Davis, California, USA
- UC Davis Lung Center, University of California Davis, CA
- VA Northern California Health Care System, 10535 Hospital Way, Mather, CA 95655, USA
- These authors contributed equally: Eva Borras, Mitchell M. McCartney
| | - Dante E. Rojas
- Mechanical and Aerospace Engineering, One Shields Avenue, University of California, Davis, Davis, California, USA
- UC Davis Lung Center, University of California Davis, CA
| | - Tristan L Hicks
- Mechanical and Aerospace Engineering, One Shields Avenue, University of California, Davis, Davis, California, USA
- UC Davis Lung Center, University of California Davis, CA
| | - Nam K Tran
- UC Davis Lung Center, University of California Davis, CA
- Department of Pathology and Laboratory Medicine, UC Davis, Sacramento CA, USA
| | - Tina Tham
- UC Davis Lung Center, University of California Davis, CA
- Department of Internal Medicine, 4150 V Street, Suite 3400, University of California, Davis, Sacramento, CA 95817, USA
| | - Maya M Juarez
- UC Davis Lung Center, University of California Davis, CA
- Department of Internal Medicine, 4150 V Street, Suite 3400, University of California, Davis, Sacramento, CA 95817, USA
| | - Lisa Franzi
- UC Davis Lung Center, University of California Davis, CA
- Department of Internal Medicine, 4150 V Street, Suite 3400, University of California, Davis, Sacramento, CA 95817, USA
| | - Richart W. Harper
- UC Davis Lung Center, University of California Davis, CA
- VA Northern California Health Care System, 10535 Hospital Way, Mather, CA 95655, USA
- Department of Internal Medicine, 4150 V Street, Suite 3400, University of California, Davis, Sacramento, CA 95817, USA
| | - Cristina E. Davis
- Mechanical and Aerospace Engineering, One Shields Avenue, University of California, Davis, Davis, California, USA
- UC Davis Lung Center, University of California Davis, CA
- VA Northern California Health Care System, 10535 Hospital Way, Mather, CA 95655, USA
| | - Nicholas J. Kenyon
- UC Davis Lung Center, University of California Davis, CA
- VA Northern California Health Care System, 10535 Hospital Way, Mather, CA 95655, USA
- Department of Pathology and Laboratory Medicine, UC Davis, Sacramento CA, USA
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Li J, Hannon A, Yu G, Idziak LA, Sahasrabhojanee A, Govindarajan P, Maldonado YA, Ngo K, Abdou JP, Mai N, Ricco AJ. Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection. ACS Sens 2023; 8:2309-2318. [PMID: 37224474 DOI: 10.1021/acssensors.3c00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We adapted an existing, spaceflight-proven, robust "electronic nose" (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using "leave-one-out" training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.
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Affiliation(s)
- Jing Li
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Ami Hannon
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - George Yu
- Variable, Inc., Chattanooga, Tennessee 37406, United States
| | - Luke A Idziak
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | | | | | - Yvonne A Maldonado
- School of Medicine, Stanford University, Stanford, California 94305, United States
| | - Khoa Ngo
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - John P Abdou
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Nghia Mai
- NASA Ames Research Center, Moffett Field, California 94035, United States
| | - Antonio J Ricco
- NASA Ames Research Center, Moffett Field, California 94035, United States
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Sharma R, Zang W, Tabartehfarahani A, Lam A, Huang X, Sivakumar AD, Thota C, Yang S, Dickson RP, Sjoding MW, Bisco E, Mahmood CC, Diaz KM, Sautter N, Ansari S, Ward KR, Fan X. Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant. JAMA Netw Open 2023; 6:e230982. [PMID: 36853606 PMCID: PMC9975913 DOI: 10.1001/jamanetworkopen.2023.0982] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/12/2023] [Indexed: 03/01/2023] Open
Abstract
Importance Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. Objective To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. Design, Setting, and Participants This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Main Outcomes and Measures Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness. Results Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy. Conclusions and Relevance The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.
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Affiliation(s)
- Ruchi Sharma
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Wenzhe Zang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Ali Tabartehfarahani
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Andres Lam
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Xiaheng Huang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Anjali Devi Sivakumar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Chandrakalavathi Thota
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Shuo Yang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
| | - Robert P. Dickson
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, University of Michigan, Ann Arbor
| | - Michael W. Sjoding
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Internal Medicine, Division of Pulmonary Critical Care Medicine, University of Michigan, Ann Arbor
| | - Erin Bisco
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Carmen Colmenero Mahmood
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Kristen Machado Diaz
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Nicholas Sautter
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Sardar Ansari
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Kevin R. Ward
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
- Department of Emergency Medicine, University of Michigan, Ann Arbor
| | - Xudong Fan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor
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