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| Name | Class |
|---|---|
| GlaxoSmithKline | INDUSTRY |
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This study proposes an approach to address an urgent unmet need in clinical practice, namely a pragmatic method of establishing what is the cause of a patient's complaint and the next steps to address this problem. In this study, the investigators will compare the proposed classification with current best practice of self-report, spirometry and FeNO. The investigators will compare the two approaches with a gold standard of deep characterisation by 3 separate diagnostic tests.
The investigators hypothesize that patients with symptoms of respiratory disease fall into one of four working groups based on accurate knowledge of three parameters, airflow, treatment use and the patient's symptoms.
In respiratory diseases, the presenting symptoms are often a combination of cough, dyspnoea and wheeze. These three symptoms can be present in a significant number of conditions, including airways disease, cardiac disease and lung parenchymal disease. Making an accurate and timely clinical diagnosis is a challenge. Furthermore, physical deconditioning and co-morbidities such as obesity often create further obstacles to diagnosis. Even in the context of a clinical diagnosis of airways disease, differentiating between asthma and COPD is often not a straightforward decision. It takes time to establish by evaluating a patient's symptoms and major risk factors like smoking or allergy suggesting a particular aetiology. The diagnosis is further refined by spirometry or measures of FeNO when available and, importantly, the patient's response to treatment (1).
There are several practical problems that make this approach less than accurate. Symptoms do not correlate with airflow limitation because co-exiting conditions like obesity and deconditioning and complicating factors such as anxiety and poor recall make symptom-based diagnosis imperfect (2). Diagnostic testing with spirometry is impractical and only provides a snapshot of lung function. This test relies on disease activity being present at the time of testing appointment. This feature is uncommon given the intermittent nature of symptoms in asthma. It is not uncommon for clinicians trying to interpret a set of lung function to hear a patient say something like "I am fine now, but I was awful two weeks ago". This means that people are often incorrectly labelled as having or not having asthma. A landmark study showed that incorrect labelling of people as having asthma but this could not be proven objectively in over 30% of patients assessed in a national study in Canada (3). The measurement of airway inflammation with FeNO is inaccurate unless treatment use is measured concurrently (4). Because of these practical problems with testing, clinicians often have to rely on symptoms to make the diagnosis of asthma.
Furthermore, in the context of an accurately established obstructive airway disease, practical issues persist. For example, differentiating between asthma and COPD, decision on referral to a secondary centre, tailoring treatment and determining if disease is controlled. Tailoring inhaled therapy to the individual patient is a further complex decision in this patient cohort. However, poor adherence to ICS/LABA treatment is common, on average it is less than 50% among patients in primary care (5-7). Therefore, the diagnosis of airways disease in primary care is inherently inaccurate. This inaccuracy means that decisions on treatment effectiveness are also inaccurate. These common but important limitations lead to overuse of corticosteroids, antibiotics and beta-agonists with poor symptom control potential medication related morbidity. It follows that diagnostic accuracy and appropriate inhaled treatment use in airways disease has real and significant implications for patient safety, adverse outcomes, cost and waste.
To address these problems the INCA team have developed algorithms to classify and align lung function, treatment use and symptoms. This data is delivered via a novel CE marked platform to non-specialists with specific "suggestion scripts" (8). The classification divides patients into 4 main groups based on whether the airway function is or is not controlled if the patient took their treatment and if they remain symptomatic (9, 10). These groups are;
This classification accounts for the common issues of poor adherence and inaccurate diagnosis in asthma, which are reported to occur as commonly as 50% and 30% respectively. The classification also accounts for some more nuanced issues that would arise if a clinician were to rely simply on assessing adherence or lung function. These include confirming that people with uncontrolled asthma have been adherent and on the other hand, even if poorly adherent, that controlled patients do not need advice on extra adherence. This classification may help a clinician to deliver a personalised, accurate and efficient consultation to people with asthma in primary care.
In this proposal the investigators will test the feasibility of this approach among patients attending community general practitioners, those newly referred for assessment in secondary care and those advanced nurse practitioner respiratory clinics with a physician's clinical diagnosis of asthma, more specifically those who have chronic respiratory symptoms who their treating physician believe to be due to asthma, but who have not yet undergone laboratory lung function testing to establish the correct diagnosis of asthma.
Hypothesis
The investigators hypothesize that the simultaneous measurement and alignment of inhaler use and airflow can be used to organise patients with clinical diagnoses of asthma into one of four groups described above. This classification can subsequently be used as the basis for adjustments to treatment and further diagnostic testing as needed. The investigators will assess the value of this approach in primary care by comparing it with the standard approach of using point in time measures with FeNO and spirometry coupled with self-report.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Respiratory | Patients attending primary care, non-specialist respiratory clinics and advanced nurse practitioner clinics with undiagnosed persisting respiratory symptoms that have been attributed to asthma by a physician. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Diagnostic testing | Diagnostic Test | Data will be uploaded to a server where algorithms will be deployed that incorporate features related to treatment use and variables of airflow will allocate the care pathway into one of the 4 pathways. Treatment will be directed by this using a validated automated decision support system |
| Measure | Description | Time Frame |
|---|---|---|
| Comparing gold standard asthma diagnostics to PEFR (L/min) | To assess the sensitivity of using serially measured, digital lung function measures (PEFR L/min) in confirming a diagnosis of asthma. | 12 weeks |
| Comparing gold standard asthma diagnostics to repeated measures of lung function FEV1/FVC | To assess the sensitivity of using serially measured, lung function FEV1/FVC in confirming a diagnosis of asthma. | 12 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Diurnal variation | Assess the degree of diurnal variation in patients with mild asthma accounting for treatment effect with bronchodilator. | 12 weeks |
| Activity levels | Measure patients daily activity level (in steps per day) |
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Inclusion Criteria:
Exclusion Criteria:
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Patients attending primary care, non-specialist respiratory clinics and advanced nurse practitioner clinics with undiagnosed persisting respiratory symptoms that have been attributed to asthma by a physician.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Elaine Mac Hale | Contact | 018093730 | elainemachale@rcsi.com | |
| Lorna Lombard | Contact | 018093787 | Lornalombard@rcsi.com |
| Name | Affiliation | Role |
|---|---|---|
| Richard Costello, Professor | Royal College of Surgeons, Ireland | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beaumont Hospital | Dublin | Ireland |
|
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Board G. GINA Report, Global Strategy for Asthma Management and Prevention. GINA Report, Global Strategy for Asthma Management and Prevention. 2016. | ||
| 31806719 | Background | McDonald VM, Clark VL, Cordova-Rivera L, Wark PAB, Baines KJ, Gibson PG. Targeting treatable traits in severe asthma: a randomised controlled trial. Eur Respir J. 2020 Mar 5;55(3):1901509. doi: 10.1183/13993003.01509-2019. Print 2020 Mar. | |
| 29756989 |
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| ID | Term |
|---|---|
| D012120 | Respiration Disorders |
| D001249 | Asthma |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| ID | Term |
|---|---|
| D012140 | Respiratory Tract Diseases |
| D001982 | Bronchial Diseases |
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
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| ID | Term |
|---|---|
| D019937 | Diagnostic Techniques and Procedures |
| ID | Term |
|---|---|
| D003933 | Diagnosis |
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Serum - periostin
|
| 12 weeks |
| Background |
| Aaron SD, Boulet LP, Reddel HK, Gershon AS. Underdiagnosis and Overdiagnosis of Asthma. Am J Respir Crit Care Med. 2018 Oct 15;198(8):1012-1020. doi: 10.1164/rccm.201804-0682CI. |
| 30339770 | Background | Heaney LG, Busby J, Bradding P, Chaudhuri R, Mansur AH, Niven R, Pavord ID, Lindsay JT, Costello RW; Medical Research Council UK Refractory Asthma Stratification Programme (RASP-UK). Remotely Monitored Therapy and Nitric Oxide Suppression Identifies Nonadherence in Severe Asthma. Am J Respir Crit Care Med. 2019 Feb 15;199(4):454-464. doi: 10.1164/rccm.201806-1182OC. |
| 28276739 | Background | Moran C, Doyle F, Sulaiman I, Bennett K, Greene G, Molloy GJ, Reilly RB, Costello RW, Mellon L. The INCATM (Inhaler Compliance AssessmentTM): A comparison with established measures of adherence. Psychol Health. 2017 Oct;32(10):1266-1287. doi: 10.1080/08870446.2017.1290243. Epub 2017 Feb 28. |
| 27587321 | Background | Sulaiman I, Seheult J, MacHale E, D'Arcy S, Boland F, McCrory K, Casey J, Bury G, Al-Alawi M, O'Dwyer S, Ryder SA, Reilly RB, Costello RW. Irregular and Ineffective: A Quantitative Observational Study of the Time and Technique of Inhaler Use. J Allergy Clin Immunol Pract. 2016 Sep-Oct;4(5):900-909.e2. doi: 10.1016/j.jaip.2016.07.009. |
| 27409253 | Background | Sulaiman I, Cushen B, Greene G, Seheult J, Seow D, Rawat F, MacHale E, Mokoka M, Moran CN, Sartini Bhreathnach A, MacHale P, Tappuni S, Deering B, Jackson M, McCarthy H, Mellon L, Doyle F, Boland F, Reilly RB, Costello RW. Objective Assessment of Adherence to Inhalers by Patients with Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2017 May 15;195(10):1333-1343. doi: 10.1164/rccm.201604-0733OC. |
| 30409819 | Background | Blakey JD, Bender BG, Dima AL, Weinman J, Safioti G, Costello RW. Digital technologies and adherence in respiratory diseases: the road ahead. Eur Respir J. 2018 Nov 22;52(5):1801147. doi: 10.1183/13993003.01147-2018. Print 2018 Nov. |
| 29301919 | Background | Sulaiman I, Greene G, MacHale E, Seheult J, Mokoka M, D'Arcy S, Taylor T, Murphy DM, Hunt E, Lane SJ, Diette GB, FitzGerald JM, Boland F, Sartini Bhreathnach A, Cushen B, Reilly RB, Doyle F, Costello RW. A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma. Eur Respir J. 2018 Jan 4;51(1):1701126. doi: 10.1183/13993003.01126-2017. Print 2018 Jan. |
| 31568927 | Background | O'Dwyer S, Greene G, MacHale E, Cushen B, Sulaiman I, Boland F, Bosnic-Anticevich S, Mokoka MC, Reilly RB, Taylor T, Ryder SA, Costello RW. Personalized Biofeedback on Inhaler Adherence and Technique by Community Pharmacists: A Cluster Randomized Clinical Trial. J Allergy Clin Immunol Pract. 2020 Feb;8(2):635-644. doi: 10.1016/j.jaip.2019.09.008. Epub 2019 Sep 27. |
| 41448794 | Derived | Gill CM, Kerr PJ, Ottewill C, Brennan V, Doherty H, Smith O, MacHale E, Cushen B, Ryan D, Greene G, Costello RW. Digitally assessed home FEV1 to identify the cause of poorly controlled asthma: a protocol paper for a prospective replicate cohort study. BMJ Open Respir Res. 2025 Dec 25;12(1):e003696. doi: 10.1136/bmjresp-2025-003696. |
| D012130 |
| Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |