Clinical trial NCT03566862: Audio Technology To Detect Lung Cancer Earlier
Official title: Audio Technology To Detect Lung Cancer Earlier
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Lung Neoplasms
Cough
Lead sponsor: NHS Greater Glasgow and Clyde
Sponsor class: OTHER
Collaborators: name: University of the West of Scotland
name: King's College Hospital NHS Trust
name: Queen Mary University of London
First posted: 2018-06-25
Enrollment: 20
Sex: ALL
Minimum age (years): 50
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Eligible participants for all 4 cohorts will be identified by the CI and associated research team by screening patients who attend respiratory clinics at QEUH with suspected lung cancer (for the "Normal Smokers" and Lung Cancer cohorts) and other established respiratory diseases (for the COPD and Other (non-COPD) chronic lung disease cohorts). Individuals will be recruited by the QEUH research nurse or CI when they attend the relevant outpatient clinics.

Brief summary: A cross-sectional study of prospectively collected cough audio recordings using spectral analysis.

Detailed description: In the UK lung cancer is the leading cause of cancer death, also, UK survival rates are poorer compared to other European countries. Lung cancer is plagued by late presentation; 70% present with advanced incurable disease and a third die within 90 days of diagnosis. As such, there is a clear and urgent need to achieve earlier diagnosis of lung cancer. Symptomatic presentation is the most common route to lung cancer diagnosis and symptoms may be present for many months before diagnosis, even in early stage disease. The most common (68%) presenting symptom is cough. Unfortunately cough is also common with other illnesses. Furthermore, a high proportion of those at high-risk of lung cancer have pre-existing cough or respiratory disease (e.g. ex- or current- smokers, patients with chronic obstructive pulmonary disease (COPD)). Cough sounds are known to vary according to underlying lung pathology and could therefore have diagnostic value. Potentially, there may be unique cough and/or respiratory sounds or patterns associated with lung cancer that are not detectable by the human audio spectrum. Identification of a tool that accurately discriminates lung cancer cough could be pivotal.

This is a prospective cross-sectional study that will involve subjective analysis of spectrograms of cough recorded from individuals with "normal" lungs, individuals at high-risk for lung cancer (COPD and other chronic lung diseases) and individuals with lung cancer. 24 hour ambulatory audio recordings will be prospectively collected from patients attending respiratory medicine clinics at Queen Elizabeth University Hospital (QEUH), Glasgow. Participants will be given a free-field lapel microphone and mp3 recorder for 24h. The Leicester Cough Monitor (LCM) will be used to extract the cough sounds from the 24h recordings. The LCM is an automated cough detection system that was developed by Dr Surinder Birring (Kings College Hospital). It uses an algorithm to automatically identify cough sounds from audio recordings, which is then able to provide data on cough frequency. As part of this process, the LCM splices out 1 second sound clips for all parts of the audio recordings that are identified as being (i) a cough sound or (ii) a non-cough sound.

Eligibility criteria: Inclusion Criteria:

* Aged 50 years and above and who are either:
* "Normal Smokers" - individuals who have presented with cough but who appear to have 'healthy' lungs (i.e. COPD, other chronic lung disease and lung cancer have been excluded after clinical assessment);
* "COPD" - individuals with a confirmed diagnosis of COPD according to established criteria;
* "Other (non-COPD) chronic lung disease" - individuals with a confirmed diagnosis of non-COPD chronic lung disease (e.g. pulmonary fibrosis, asthma);
* "Lung cancer" - individuals with confirmed diagnosis of lung cancer including disease in the lungs and an active cough

Exclusion Criteria:

* Participants with an active or recent lung infection, as defined by either the production of purulent sputum associated with systemic symptoms or fever, and/or the receipt of antibiotics for lung infection or acute exacerbation over the 2 weeks preceding the date of consent
* Participants who are unable to provide informed consent
* Participants who are receiving/have previously received radiotherapy to the lungs
* Participants who are currently receiving chemotherapy

Primary outcomes:
measure: Spectral centroid (Hz) measurement
timeFrame: 12 months
Secondary outcomes:
measure: Spectral bandwith (Hz) measurement
timeFrame: 12 months
measure: Spectral crest factor (Hz) measurement
timeFrame: 12 months
measure: Spectral flatness (adimensional) measurement
timeFrame: 12 months
measure: Spectral flux (Watts²) measurement
timeFrame: 12 months
measure: Spectral roll-off (Hz) measurement
timeFrame: 12 months
measure: Ratio f50 vs f90 (adimensional) measurement
timeFrame: 12 months
measure: Spectral peak entropy (adimensional measurement)
timeFrame: 12 months
measure: Spectral Renyi entropy (adimensional) measurement
timeFrame: 12 months
measure: Spectral kurtosis (adimensional) measurement
timeFrame: 12 months
measure: Spectral skewness (adimensional) measurement
timeFrame: 12 months
measure: Spectral entropy (adimensional) measurement
timeFrame: 12 months
Other outcomes:
Locations:
city: Glasgow
country: United Kingdom
facility: Queen Elizabeth University Hospital Glasgow
Study officials:
affiliation: NHSGG&C
name: Kevin G Blyth, MD
role: PRINCIPAL_INVESTIGATOR
References: