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Respiratory disease is the third biggest cause of death in the United Kingdom (UK) with 20% of all deaths attributed to
respiratory disease and lung cancer. Respiratory disease is also one of the commonest causes of emergency
hospital admissions (>700,000 per year) and winter bed pressures faced by the National Health Service (NHS). Prior
to COVID-19,lung conditions were already costing the NHS around ÂŁ9.9 billion each year. There are strong links
between lung disease,social deprivation and health inequalities with environmental drivers of lung disease such as
smoking,outdoor pollution,housing and occupational hazards more prevalent in socio-economically deprived
communities. The NHS Long Term Plan,which outlines the actions to improve the nation’s health outcomes,identified
respiratory disease as a national clinical priority.
Spirometry is an essential diagnostic procedure recommended for the diagnosis and monitoring of chronic
obstructive pulmonary disease (COPD) and asthma,the commonest long-term respiratory conditions. However,spirometry provision in primary care is suboptimal. Only 13.4% of spirometry performed in primary care meet
international criteriaÍľ more than 40% fail at least one quality criteria. There are low levels of confidence in identifying
technical errors or interpreting spirometry and there are poor levels of agreement in spirometry interpretation between
primary care and specialist respiratory physicians. Important consequences include under-diagnosis,misdiagnosis
and unnecessary referral to secondary care.
The Association for Respiratory Technology and Physiology (ARTP) provide training and accreditation in the UK for
clinicians. Accreditation can be for performing spirometry test only,interpretation of spirometry,or both,with the aim
that ARTP accredited clinicians are competent to perform quality assured spirometry. The NHS Long Term Plan
ambition was that all those performed spirometry would be ARTP accredited.
ArtiQ.Spiro is a decision support software that combines two sub-components – one focussing on quality
assessment,and one on spirometry interpretation. The software is intended to be used as an adjunct to spirometry
to assist with the grading of spirometry quality and the interpretation of spirometry by providing the probability of six
categories.
Quality Assessment
International guidelines comprise objective criteria for ensuring good quality spirometry but also requires manual
visual inspection introducing a degree of subjectivity. Even in cohorts of trained and accredited staff,there is inter-rater
variability when assessing spirometry quality. ArtiQ.Spiro leverages deep learning methods to perform the subjective
elements of spirometry quality assessment,as well as implementing the objective criteria from international
guidance. The AI component of the software (termed ArtiQ.QC) is trained to mimic the subjective visual inspection of
data usually performed by technicians. Spirometry measurements from the National Health and Nutritional
Examination Survey (NHANES 2011-12) were used as training data. Each measurement was assessed by multiple
experienced technicians to determine whether the data quality was acceptable. In total the NHANES dataset
contained 36,873 measurements,of which 54% were acceptable. Over 29,000 measurements were used to train a
convolutional neural network,with the remainder used for validation/testing.
Spirometry Interpretation
The spirometry interpretation component of ArtiQ.Spiro is delivered by ArtiQ.PFT. This is a decision support software
focusing on the diagnostic interpretation of pulmonary function tests and is currently the only CE-marked clinical
decision support software incorporating AI in the field of respiratory physiology diagnostics. It uses a random forest
machine learning model to estimate the probability of disease from the eight commonest categories (7 diseases +
normal lung function) detectable with lung function testing. 1430 cases,where the final diagnosis was known,were
used to train the random forest model. For use in primary care settings,the diagnostic support software ArtiQ.Spiro
has been adapted to identify six categories – Asthma,COPD,interstitial lung disease (ILD),normal lung function,other obstructive disease (such as cystic fibrosis and bronchiectasis),and other unidentifiable disease – using the
same training set as for ArtiQ.PFT (1430 cases of spirometry-only data). The rationale for reducing the number of A possible limitation to the ArtiQ.Spiro is that the training set for interpretation only included patients undergoing full
lung tests in hospital lung function laboratories where the lung function skillset of the practitioner is likely to be higher
than seen in primary care. Furthermore,the patient population was almost exclusively Caucasian
The NHS Long Term Plan (LTP) has identified quality assured spirometry as a clinical priority through investment into
training more community staff to perform and interpret spirometry more accurately. However,this process takes time
and disagreement over spirometry interpretation can still occur between trained staff. The purpose of this study is to
evaluate whether an AI decision support software improves the diagnostic accuracy and quality assessment of
spirometry by primary care clinicians.
categories to identify is based on the reduced lung function dataset available when working in a primary care
environment compared to a hospital-based pulmonary function testing lab.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
Interventional type: Other;
You can take part if:
You may not be able to take part if:
Clinicians who have completed specialist training in respiratory medicine and recognised by the General Medical Council with a right to practise as a consultant in respiratory medicine
Below are the locations for where you can take part in the trial. Please note that not all sites may be open.
The study is sponsored by GUY'S AND ST THOMAS' NHS FOUNDATION TRUST and funded by NHS ENGLAND .
Your feedback is important to us. It will help us improve the quality of the study information on this site. Please answer both questions.
Read full details
for Trial ID: CPMS 57579
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