A Study to Understand the Accuracy of a New Breathing Device Compared to Standard Testing for Asthma and COPD

Asthma (Diagnosis), COPD (Chronic Obstructive Pulmonary Disease)

Asthma, COPD, End tidal CO2, Diagnostics, Machine learning

The diagnosis of either asthma or chronic obstructive pulmonary disease (COPD) is currently based on a combination of symptoms, different lung tests and sometimes a 'trial by treatment'. Both COPD and Asthma tests currently include forced breathing using a test known as spirometry which can be difficult and uncomfortable for people to perform and needs expert interpretation. Asthma can require multiple tests in sequence which can make the process of diagnosis long and inconvenient for patients. In the UK, there have been challenges providing enough testing for COPD and asthma, in part because the tests are challenging to provide in the community to everyone who needs one. The STARDUST study aims to test and develop new ways of diagnosing asthma and COPD that are quick, accurate and easy for patient and healthcare professionals to perform. People who have been referred for routine lung testing will breathe normally in and out into a handheld device called the N-Tidal Handset for 75 seconds. This device measures how the level of carbon dioxide (CO2) changes in the breath as people breath through it. The information gathered on the N-Tidal Handsets, called breath records or capnograms, will not be used to alter the diagnosis of any participants in the study. After the capnograms have been collected, the research team will test 'algorithms' that have been developed using artificial intelligence, to see if they can accurately identify the correct diagnosis. Information collected in the study will also be used to make improvements in these algorithms. If confirmed to be accurate, these algorithms could be used in clinical practice to help healthcare professionals make faster, more accessible and accurate diagnoses, especially in settings like GP clinics in the community where access to specialist tests may be limited.

COPD is one of the leading causes of disability and in the top five causes of death worldwide. Early diagnosis and treatment are essential to slow the decline of lung function and improve quality of life. The current standard for diagnosing COPD is spirometry; an effort-dependent test that requires supervised use, by a specifically trained medical professional, in a clinic-based environment. It is estimated that up to 2/3 of cases are undiagnosed suggesting that there is a significant care gap in the ability to accurately diagnose COPD. Asthma is a chronic respiratory disease, characterised by airway inflammation and hyper-responsiveness. In 2019 affected an estimated 262 million people. Early diagnosis and treatment are important to reduce symptoms, exacerbations and mortality and to improve overall quality of life. The confirmation of reversible airflow obstruction using pre- and post-bronchodilator (BD) spirometry is needed to confirm a diagnosis in those suspected as having asthma without evidence of inflammation in the airways or blood. Spirometry can be challenging for patients to perform, is time consuming and can be challenging to deliver at the scale required by the population. TidalSense have been developing respiratory diagnostic devices that could offer simpler, safe, fast, reliable and precise tests to diagnose both COPD and asthma. It is anticipated that successfully meeting the primary objectives of this study will result in data on the diagnostic accuracy of the COPD diagnostic models and could lead to the development and improvement of all diagnostic algorithms used in the study. This may contribute to a range of future clinical benefits: * Accurate diagnosis of asthma and/or COPD * Accurate detection of severe COPD in patients who are likely or highly likely to have COPD * Improved ease of testing for asthma and/or COPD for patients and healthcare professionals * Clearer differentiation between asthma and COPD in patients with overlapping symptoms These benefits could enable quicker testing, improve patient experience (relaxed breathing instead of forced breathing), and improve patient access to testing compared to current diagnostic pathways and reduce misdiagnosis.

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United Kingdom