Non-invasive Detection of Pneumonia in Context of Covid-19 Using Gas Chromatography - Ion Mobility Spectrometry (GC-IMS)
- Conditions
- Respiratory DiseaseCOVID-19
- Registration Number
- NCT04329507
- Lead Sponsor
- NHS Lothian
- Brief Summary
On Dec 31, 2019, a number of viral pneumonia cases were reported in China. The virus causing pneumonia was then identified as a new coronavirus called SARS-CoV-2. Since this time, the infection called coronavirus disease 2019 (COVID-19) has spread around the world, causing huge stress for health care systems. To diagnose this infection, throat and nose swabs are taken. Unfortunately, the results often take more than 24 hrs to return from a laboratory. Speeding diagnosis up would be of great help.
This study aims to look at the breath to find signs that might allow clinicians to diagnose the coronavirus infection at the bedside, without needing to send samples to the laboratory. To do this, the team will be using a machine called a BreathSpec which has been adapted to fit in the hospital for this purpose.
- Detailed Description
Analysis of volatile organic compounds (VOCs) in exhaled breath is of increasing interest in the diagnosis of lung infection. Over 2,000 VOCs can be detected through gas chromatography and mass spectrometry (GC-MS); patterns of VOC detected can offer information on chronic obstructive pulmonary disease, asthma, lung cancer and interstitial lung disease. Unfortunately, GC-MS while highly sensitive cannot be done at the bedside and at best takes hours to prepare samples, run the analysis and then interpret the results.
Compared with other methods of breath analysis, ion mobility spectrometry (IMS) offers a tenfold higher detection rate of VOCs. By coupling an ion mobility spectrometer with a GC column, GC-IMS offers immediate twofold separation of VOCs with visualisation in a three-dimensional chromatogram. The total analysis time is about 300 seconds and the equipment has been miniaturised to allow bedside analysis.
The BreathSpec machine has been previously used to study both radiation injury in patients undergoing radiotherapy at the Edinburgh Cancer Centre (REC ref 16-SS-0059, as part of the H2020 TOXI-triage project, http://www.toxi-triage.eu/) and pneumonia in patients presenting to the ED of the Royal Infirmary of Edinburgh (REC ref 18-LO-1029). This work has developed artificial intelligence methodology that allows rapid analysis of the vast amount of data collected from these breath samples to identify signatures that may indicate a particular pathological process such as pneumonia or radiation injury.
The TOXI-triage project showed that the BreathSpec GC-IMS could rapidly triage individuals to identify those who had been exposed to particular volatile liquids in a mass casualty situation (http://www.toxi-triage.eu/).
A pilot trial assessed chest infections at the Acute Medical Unit of the Royal Liverpool University Hospital. The final diagnostic model permitted fair discrimination between bacterial chest infections and chest infections due to other agents with an area under the receiver operator characteristic curve (AUC-ROC) of 0.73 (95% CI 0.61-0.86). The summary test characteristics were a sensitivity of 62% (95% CI 41-80%) and specificity of 80% (95% CI 64 - 91%) \[8\].
This was expanded in the EU H2020 funded "Breathspec Study" which aimed to differentiate breath samples from patients with bacterial or viral upper or lower respiratory tract infection. Over 1220 patients were recruited, with 191 patients identified as definitely bacterial infection and 671 classed as definitely not bacterial. Virology was undertaken on all patients, with 259 patients confirmed viral infection. Date processing is still on going to determine how well they can be distinguished using this methodology. More than 100 patients were recruited to this study in Edinburgh. Since then, artificial intelligence has been incorporated into our analytical processes, permitting faster and more refined analysis.
Our ambition is that this technology will identify a signature of Covid-19 pneumonia or within 10 min in non-invasively collected breath samples to allow triage of patients into high and low risk categories for Covid-19. This will allow targeting of scarce resources and complex protocols associated with high risk patients including personal protective equipment (PPE), cohorting, and dedicated medical and nursing personel.
A healthy volunteer arm was added in July 2020 - 40 particpants
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 225
-
≥18 years old with clinical features consistent with pneumonia or chest infection due to SARS-CoV-2 AND
- presenting to the Royal Infirmary of Edinburgh where they are swabbed and triaged for Covid-19.
- Inability to provide informed consent
- Age 17 years or less
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method To perform a study in patients with clinical features of pneumonia/chest infection to identify a signature of Covid-19 pneumonia in patients exposed to SARS-CoV-2, compared to unexposed patients or those without. up to daily during hospital admission breath sample collection
- Secondary Outcome Measures
Name Time Method Detection of markers of Covid-19 pneumonia in non-invasive breath samples. multiple samples up to 60 days breath sample collection
Relationship of this biomarker signature to the presence of SARS-CoV-2 in nasal and throat swabs. multiple samples up to 60 days breath sample collection
Subsequently, the signature's relationship to other biomarkers of SARS-CoV-2 infection which are currently being explored multiple samples up to 60 days breath sample collection
In a smaller group of participants, ideally daily non-invasive breath samples will be collected to determine if there are changes between SARS-CoV-2 positive patients and those that are negative until hospital discharge or undue participant burden . multiple samples up to 60 days breath sample collection
Trial Locations
- Locations (1)
NHS Lothian
🇬🇧Edinburgh, United Kingdom