Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Harrison.ai Chest X-Ray Solution.
- Who it may be relevant to
- Registry conditions: Cancer, Cardiovascular, Respiratory. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United Kingdom
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital. The aim of the study is to find out if using a computer programme can help doctors diagnose heart and lung problems from chest x-rays. We want to compare how many people are diagnosed with heart or lung problems for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Detailed description
SEARCH-ED is a research study which is running in Emergency Department (ED) of the Queen Elizabeth University Hospital.
The aim of the study is to find out if using an artificial intelligence (AI) computer programme can help doctors diagnose heart and lung problems from chest x-rays. The computer programme is made by Harrison.ai. It is approved for use in the United Kingdom (UK), United States of America (US) and the European Union (EU). Studies have been carried out previously to make sure it is safe to use and that it can detect signs of heart and lung problems.
Many people who come to ED have a chest x-ray. Chest x-rays can show signs of heart or lung problems, which might be causing a patient's symptoms. All doctors can interpret chest x-rays. However, doctors who specialise in interpreting scans (radiologists) also provide an expert report for chest x-rays, describing what they have found. It can take a long time for chest x-ray reports to come back. Sometimes, doctors might miss signs of heart or lung problems.
We want to see if using a computer programme to help doctors interpret chest x-rays could lead to more patients getting an accurate diagnosis. We want to compare how many people are diagnosed with heart or lung problems (Chronic obstructive pulmonary disease \[COPD\], heart failure or lung cancer) for the first time when doctors have access to the computer programme results, in comparison to when they don't.
Patients older than 18 who have a chest x-ray in ED will be included.
Patients with chest x-rays flagged by the computer programme for heart failure or COPD will be invited to an outpatient clinic for further assessment post-discharge, providing they have not been referred for testing or had testing previously.
All patients with chest x-rays flagged for lung cancer will be reviewed and acted on by the study radiologist.
The study consists of 1) a retrospective component; 2) a prospective live trial; 3) a qualitative evaluation of acceptability to patients and clinicians, and 4) a health economic analysis.
Interventions
- Device Harrison.ai Chest X-Ray Solution
The Harrison.ai CXR module is an AI-driven clinical decision support tool that is designed to augment clinical interpretation of CXRs. It is a Class IIb CE-marked device which is able to detect up to 124 findings on a CXR.
Primary outcome measures
- Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation. [Time frame: 12 months]
Secondary outcome measures (11)
- Duration of admission during index hospitalisation [Time frame: 12 months]
- Time to initiation of guideline-based, long-term therapy for Chronic obstructive pulmonary disease (COPD) and Heart Failure. [Time frame: 12 months]
- Time to diagnostic testing for Heart Failure, COPD and lung cancer (echocardiography, spirometry, CT). [Time frame: 12 months]
- Time to inpatient or outpatient specialist review and confirmation of lung cancer, COPD or Heart Failure [Time frame: 12 months]
- Acceptability of AI-supported interpretation of Chest X-Ray for Emergency Department clinicians pre and post intervention using Theoretical Framework of Acceptability (TFA) [Time frame: Baseline and 12 months]
- Readmission rate within 90 days [Time frame: 3 months]
- Proportion of patients with new diagnosis of lung cancer detected by an AI-Chest X-Ray algorithm [Time frame: 12 months]
- Proportion of patients with new diagnosis of COPD detected by an AI-Chest X-Ray algorithm [Time frame: 12 months]
- Proportion of patients with clinically-confirmed known diagnosis of lung cancer, Heart Failure and COPD detected by an AI-Chest X-Ray algorithm [Time frame: 12 months]
- Percentage of Chest X-Rays not identified by an AI-CXR algorithm that have a subsequent diagnosis of Heart Failure, COPD or lung cancer within 6 months of index imaging (Emergency Department Chest X-Ray). [Time frame: 6 months]
- Statistical analysis of model performance e.g. sensitivity, specificity, positive and negative predictive value [Time frame: 12 months]
Eligibility criteria
Inclusion criteria
Unconsented Use of Harrison CXR Algorithm in Emergency Department (ED):
- Frontal Chest X-Ray (CXR) (AP or PA) acquired in the Queen Elizabeth University Hospital (QEUH) ED
- Patients aged 18 or over
- Appropriate meta data (DICOM) to allow for Harrison CXR processing and secondary capture report provision.
Patient Focus Groups:
- Aged 18 or over
- Able to provide written, informed consent in English.
Clinician Focus Groups:
- Aged 18 or over
- Able to provide written, informed consent in English.
- Working as a doctor, advanced nurse practitioner or advanced clinical practitioner in ED, radiology or downstream medical specialties
- For post-implementation focus groups only, must have at least 4 months experience of working with Harrison CXR algorithm.
Diagnostic Clinic:
- Patients without terminal illness or advanced frailty
- Usual healthcare provider based in NHS GGC
Exclusion criteria
Applies to use of unconsented CXRs:
\- Patient has requested that they are removed from the study, or has objected to the use of AI in their routine clinical care and this has been subsequently upheld by the health board.
Applies to invitation to combined diagnostic clinic:
- Patients not available to follow up, including patients i.e. whose the patient's usual care (or onward care following index admission) is out-with NHS GGC.
- Patients who have been referred to palliative care for end-stage disease, or patients with severe frailty (i.e. bedbound) will not be invited to the combined diagnostic clinic
For Patient and Clinician Focus Groups:
- Unable to provide informed written consent in English
- Aged <18
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Non-randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Diagnostic
Study locations
United Kingdom · 1 center
- Queen Elizabeth University Hospital — Glasgow
Identifiers
NCT: NCT07391280 · INGN23AE079 · 360783