Strategy for EArly Recognition of Cancer, COPD & Heart Failure in the Emergency Department

NCT07391280 | Not Yet Recruiting

• 2 other identifiers: INGN23AE079360783
• Study Type: interventional
• Target: 25,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Cancer; Cardiovascular; Respiratory

Keywords

Artificial Intelligence; Chest X-Ray; Lung Cancer; Heart Failure; COPD

Outcome Measures

Primary Outcomes (1)

• Proportion of patients identified with a confirmed new diagnosis of heart failure, based on subsequent clinical assessment and guideline-based investigation.

  12 months

Secondary Outcomes (11)

• Duration of admission during index hospitalisation

  12 months

• Time to initiation of guideline-based, long-term therapy for Chronic obstructive pulmonary disease (COPD) and Heart Failure.

  12 months

• Time to diagnostic testing for Heart Failure, COPD and lung cancer (echocardiography, spirometry, CT).

  12 months

• Time to inpatient or outpatient specialist review and confirmation of lung cancer, COPD or Heart Failure

  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)

  Baseline and 12 months

Study Arms (2)

AE On Block

EXPERIMENTAL

Chest X-ray AI results will be available to the treating clinician

Device: Annalise.AI Chest X-Ray Solution

AE off Block

ACTIVE COMPARATOR

Chest X-ray AI results will not be available to the treating clinician

Device: Annalise.AI Chest X-Ray Solution

Interventions

Annalise.AI Chest X-Ray Solution DEVICE

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.

AE On Block; AE off Block

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Unconsented Use of AE 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 Annalise Enterprise 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 AE algorithm.
• Diagnostic Clinic:
• Patients without terminal illness or advanced frailty
• Usual healthcare provider based in NHS GGC

You may not qualify if:

• 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

Sponsors & Collaborators

• NHS Greater Glasgow and Clyde: lead
• University of Glasgow: collaborator

MeSH Terms

Conditions

Neoplasms; Lung Neoplasms; Heart Failure; Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Lung Diseases; Respiratory Tract Diseases; Heart Diseases; Cardiovascular Diseases; Lung Diseases, Obstructive; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• David J Lowe

  University of Glasgow

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Clare Dolan

CONTACT

0141 314 4328; clare.dolan3@nhs.scot

Dervla Carroll

CONTACT

dervla.carroll@glasgow.ac.uk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: CARE PROVIDER
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 13, 2026
• First Posted: February 5, 2026
• Study Start: February 16, 2026
• Primary Completion (Estimated): December 14, 2026
• Study Completion (Estimated): December 14, 2026
• Last Updated: February 5, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, CSR
• Time Frame: After publication of results
• Access Criteria: Bone fide collaboration request