Discriminatory Ability of Gas Chromatography-Ion Mobility Spectrometry to Identify Patients Hospitalized With COVID-19 and Predict Prognosis.

Background: Rapid diagnostic and prognostic tests for coronavirus disease (COVID-19) are urgently required. We aimed to evaluate the diagnostic and prognostic ability of breath analysis using gas chromatography-ion mobility spectrometry (GC-IMS) in hospitalized patients with COVID-19. Methods: Between February and May 2021, we took 1 breath sample for analysis using GC-IMS from participants who were admitted to the hospital for COVID-19, participants who were admitted to the hospital for other respiratory infections, and symptom-free controls, at the University Hospitals of Leicester NHS Trust, United Kingdom. Demographic, clinical, and radiological data, including requirement for continuous positive airway pressure (CPAP) ventilation as a marker for severe disease in the COVID-19 group, were collected. Results: A total of 113 participants were recruited into the study. Seventy-two (64%) were diagnosed with COVID-19, 20 (18%) were diagnosed with another respiratory infection, and 21 (19%) were healthy controls. Differentiation between participants with COVID-19 and those with other respiratory tract infections with GC-IMS was highly accurate (sensitivity/specificity, 0.80/0.88; area under the receiver operating characteristics curve [AUROC], 0.85; 95% CI, 0.74-0.96). GC-IMS was also moderately accurate at identifying those who subsequently required CPAP (sensitivity/specificity, 0.62/0.80; AUROC, 0.70; 95% CI, 0.53-0.87). Conclusions: GC-IMS shows promise as both a diagnostic tool and a predictor of prognosis in hospitalized patients with COVID-19 and should be assessed further in larger studies.

Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study.

BACKGROUND: There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS). METHODS: Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data. FINDINGS: Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1). INTERPRETATION: These two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons. FUNDING: MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29.

Use of headspace-gas chromatography-ion mobility spectrometry to detect volatile fingerprints of palm fibre oil and sludge palm oil in samples of crude palm oil.

OBJECTIVE: The addition of residual oils such as palm fibre oil (PFO) and sludge palm oil (SPO) to crude palm oil (CPO) can be problematic within supply chains. PFO is thought to aggravate the accumulation of monochloropropanediols (MCPDs) in CPO, whilst SPO is an acidic by-product of CPO milling and is not fit for human consumption. Traditional targeted techniques to detect such additives are costly, time-consuming and require highly trained operators. Therefore, we seek to assess the use of gas chromatography-ion mobility spectrometry (GC-IMS) for rapid, cost-effective screening of CPO for the presence of characteristic PFO and SPO volatile organic compound (VOC) fingerprints. RESULTS: Lab-pressed CPO and commercial dispatch tank (DT) CPO were spiked with PFO and SPO, respectively. Both additives were detectable at concentrations of 1% and 10% (w/w) in spiked lab-pressed CPO, via seven PFO-associated VOCs and 21 SPO-associated VOCs. DT controls could not be distinguished from PFO-spiked DT CPO, suggesting these samples may have already contained low levels of PFO. DT controls were free of SPO. SPO was detected in all SPO-spiked dispatch tank samples by all 21 of the previously distinguished VOCs and had a significant fingerprint consisting of four spectral regions.

Increasing conclusiveness of clinical breath analysis by improved baseline correction of multi capillary column - ion mobility spectrometry (MCC-IMS) data.

Current challenges of clinical breath analysis include large data size and non-clinically relevant variations observed in exhaled breath measurements, which should be urgently addressed with competent scientific data tools. In this study, three different baseline correction methods are evaluated within a previously developed data size reduction strategy for multi capillary column - ion mobility spectrometry (MCC-IMS) datasets. Introduced for the first time in breath data analysis, the Top-hat method is presented as the optimum baseline correction method. A refined data size reduction strategy is employed in the analysis of a large breathomic dataset on a healthy and respiratory disease population. New insights into MCC-IMS spectra differences associated with respiratory diseases are provided, demonstrating the additional value of the refined data analysis strategy in clinical breath analysis.

Breath analysis: translation into clinical practice.

Breath analysis in respiratory disease is a non-invasive technique which has the potential to complement or replace current screening and diagnostic techniques without inconvenience or harm to the patient. Recent advances in ion mobility spectrometry (IMS) have allowed exhaled breath to be analysed rapidly, reliably and robustly thereby facilitating larger studies of exhaled breath profiles in clinical environments. Preliminary studies have demonstrated that volatile organic compound (VOC) breath profiles of people with respiratory disease can be distinguished from healthy control groups but there is a need to validate, standardise and ensure comparability between laboratories before real-time breath analysis becomes a clinical reality. It is also important that breath sampling procedures and methodologies are developed in conjunction with clinicians and the practicalities of working within the clinical setting are considered to allow the full diagnostic potential of these techniques to be realised. A protocol is presented, which has been developed over three years and successfully deployed for quickly and accurately collecting breath samples from 323 respiratory patients recruited from 10 different secondary health care clinics.

Data size reduction strategy for the classification of breath and air samples using multicapillary column-ion mobility spectrometry.

Ion mobility spectrometry combined with multicapillary column separation (MCC-IMS) is a well-known technology for detecting volatile organic compounds (VOCs) in gaseous samples. Due to their large data size, processing of MCC-IMS spectra is still the main bottleneck of data analysis, and there is an increasing need for data analysis strategies in which the size of MCC-IMS data is reduced to enable further analysis. In our study, the first untargeted chemometric strategy is developed and employed in the analysis of MCC-IMS spectra from 264 breath and ambient air samples. This strategy does not comprise identification of compounds as a primary step but includes several preprocessing steps and a discriminant analysis. Data size is significantly reduced in three steps. Wavelet transform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reduction with down to 50 variables relevant to the goal of analysis. The influence and compatibility of the data reduction tools are studied by applying different settings of the developed strategy. Loss of information after preprocessing is evaluated, e.g., by comparing the performance of classification models for different classes of samples. Finally, the interpretability of the classification models is evaluated, and regions of spectra that are related to the identification of potential analytical biomarkers are successfully determined. This work will greatly enable the standardization of analytical procedures across different instrumentation types promoting the adoption of MCC-IMS technology in a wide range of diverse application fields.