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.
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 Annalise Enterprise. 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.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
The Annalise Enterprise (AE) 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.
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
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.
For Chronic obstructive pulmonary disease (COPD), this will be defined as first prescription of combined long acting beta agonist (LABA)/long acting muscarinic antagonist (LAMA) inhaler or LABA/LAMA/inhaled corticosteroid (single or split) inhaler therapy. For Heart Failure , this will be defined as first prescription of either a) a renin-angiotensin system inhibitors, b) a beta blocker, or c) an SLGT2 inhibitor.
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)
We will ask clinicians what they think of using AI for Chest X-Rays
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
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DIAGNOSTIC
Masking
SINGLE
Enrollment
25,000
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