Spirometry Interpretation Performance of Primary Care Clinicians With/Without AI Software (SPIRO-AID)
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ClinicalTrials.gov Identifier: NCT05933694 |
Recruitment Status :
Recruiting
First Posted : July 6, 2023
Last Update Posted : February 16, 2024
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Condition or disease | Intervention/treatment | Phase |
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Lung Disease | Other: Artificial Intelligence-powered Spirometry Interpretation Report | Not Applicable |
This is a randomised controlled study to evaluate the effects of AI support software on the performance of primary care clinicians in the interpretation of spirometry. Clinicians will be provided with a clinical dataset of 50 entirely anonymous, previously recorded real-world spirometry records to interpret and will be asked to complete specific questions about diagnosis and quality assessment. The records will be randomly selected from a database comprising spirometry records from 1122 patients undergoing spirometry in primary care and community -based respiratory clinics in Hillingdon borough between 2015-2018.
Participating clinicians will be allocated at random to receive either spirometry records alone or spirometry records with the addition of an AI spirometry interpretation eport. The clinical spirometry records will be de-identified (name, date of birth, address, postcode, occupation, GP, medications data removed), by a member of the clinical care team.
Study participants (participating clinicians) will independently examine the same 50 spirometry records through an online platform. For each spirometry record, the primary care clinician participant will answer questions about technical quality, pattern interpretation, preferred diagnosis, differential diagnosis and self-rated confidence with these answers.
The study statistician will be blinded to treatment allocation up to completion of analysis and interpretation.
The reference standards for spirometry technical quality and pattern interpretation will be made by a senior experienced respiratory physiologist but without access to AI report.
The reference standard for diagnosis will be made by a panel of three respiratory specialists from the clinical care team with access to medical notes and results of relevant investigations but without access to AI report.
Study Type : | Interventional (Clinical Trial) |
Estimated Enrollment : | 228 participants |
Allocation: | Randomized |
Intervention Model: | Parallel Assignment |
Masking: | Double (Investigator, Outcomes Assessor) |
Primary Purpose: | Health Services Research |
Official Title: | A Randomized Controlled Trial Comparing Performance of Primary Care Clinicians in the Interpretation of SPIROmetry With or Without Artificial Intelligence Decision Support Software |
Actual Study Start Date : | June 27, 2023 |
Estimated Primary Completion Date : | June 27, 2024 |
Estimated Study Completion Date : | September 30, 2024 |
Arm | Intervention/treatment |
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No Intervention: Control
Participants to report 50 spirometry records alone
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Experimental: Intervention
Participants report the same 50 spirometry records provided in the control arm with an artificial intelligence-powered spirometry interpretation report
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Other: Artificial Intelligence-powered Spirometry Interpretation Report
A report generated by artificial intelligence powered software that assessed technical quality of spirometry and estimates the diagnostic probability of six categories: COPD/Asthma/ILD/ Normal/Other obstructive/Other Unidentified
Other Name: ArtiQ.Spiro |
- Preferred Diagnostic Performance [ Time Frame: Six months ]A correct case is where the preferred diagnosis matches the reference final diagnosis. Units will be percentage of total cases that are correct.
- Pattern interpretation [ Time Frame: Six months ]A correct case is where the participants' selected pattern matches the reference pattern. Options are: Normal, Airflow obstruction, Possible restriction or non-specific pattern, Possible Mixed Disorder. Units will be percentage of total cases that are correct.
- Differential diagnostic performance [ Time Frame: Six months ]A correct case is where the preferred or differential diagnosis matches the reference final diagnosis. Units will be percentage of total cases that are correct.
- Quality assessment performance [ Time Frame: Six months ]A correct case is where the participant's quality grade matches the reference quality grade. Options are: Acceptable (Grade A/B) or Not Acceptable (Grades C/D/E/F/U). Units will be percentage of total cases that are correct.
- Pattern interpretation self-rated confidence [ Time Frame: Six months ]Pattern interpretation self-rated confidence will be measured on a visual analogue scale (0-10) where 0 = not confident at all; 10= very confident)
- Diagnostic self-rated confidence [ Time Frame: Six months ]Diagnostic self-rated confidence will be measured on a visual analogue scale (0-10) where 0 = not confident at all; 10= very confident)
- Quality Assessment self-rated confidence [ Time Frame: Six months ]Quality Assessment self-rated confidence will be measured on a visual analogue scale (0-10) where is 0 = not confident at all; 10= very confident)
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Ages Eligible for Study: | 18 Years to 99 Years (Adult, Older Adult) |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | Yes |
Inclusion Criteria:
- Clinicians working in primary care (for at least 50% of their job plan) in the UK, who refer for or perform spirometry (typically GP, practice nurse)
- Able to access spirometry traces on study platform
- Provide written informed consent via study platform
Exclusion Criteria:
1. Clinicians who have completed specialist training in respiratory medicine and recognised by the General Medical Council with a right to practise as a NHS consultant in respiratory medicine
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05933694
Contact: Ethaar El-Emir, PhD | 01895 823737 ext 85952 | e.el-emir@rbht.nhs.uk | |
Contact: George Edwards, MSc | 01895 823737 | G.Edwards2@rbht.nhs.uk |
United Kingdom | |
Royal Brompton & Harefield Hospitals | Recruiting |
Uxbridge, United Kingdom, UB9 6JH | |
Contact: Ethaar El-Emir, PhD 01895 823737 ext 85952 e.el-emir@rbht.nhs.uk | |
Contact: George Edwards, MSc 01895 823737 G.Edwards2@rbht.nhs.uk | |
Principal Investigator: William Man |
Principal Investigator: | William Man | Royal Brompton & Harefield Hospitals |
Responsible Party: | Royal Brompton & Harefield NHS Foundation Trust |
ClinicalTrials.gov Identifier: | NCT05933694 |
Other Study ID Numbers: |
323361 |
First Posted: | July 6, 2023 Key Record Dates |
Last Update Posted: | February 16, 2024 |
Last Verified: | February 2024 |
Individual Participant Data (IPD) Sharing Statement: | |
Plan to Share IPD: | No |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
Lung Diseases Respiratory Tract Diseases |