Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04329507 |
| Other study ID # |
282014 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 25, 2020 |
| Est. completion date |
May 30, 2021 |
Study information
| Verified date |
August 2021 |
| Source |
NHS Lothian |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial 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.
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
