Use of Artificial Intelligence-Guided Echocardiography to assIst cardiovascuLar Patient managEment

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

612 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-02-24

Study Completion Date

2026-06-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Heart Failure and valvular heart disease are disproportionate problems in rural and remote Australia (RRA). Echocardiography is the best imaging investigation, and essential for management, but access to this essential test shows huge geographic variations, primarily because of dependence on expert acquisition. This trial seeks to demonstrate the effectiveness of artificial intelligence-based echocardiography for triage and management of patients with known or suspected heart disease in RRA.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

ECG Low Ejection Fraction Detection and Guiding in AI Navigated Treatment Era

NCT06968533

Heart Failure Ventricular Dysfunction, Left Artificial Intelligence +3 more

ENROLLING_BY_INVITATION NA

Low Ejection Fraction in Single Lead ECG

NCT05010655

Left Ventricular Dysfunction Ventricular Dysfunction Heart Diseases +1 more

COMPLETED

Artificial Intelligence-assisted Diagnosis and Prognostication in Low Ejection Fraction Using Electrocardiograms

NCT05117970

Left Ventricular Systolic Dysfunction (Disorder)

COMPLETED NA

AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor

NCT06699056

Ventricular Ejection Fraction LVF LV Dysfunction +12 more

RECRUITING

Detection of Heart Conditions With Single Lead ECG Using Artificial Intelligence

NCT04400435

Left Ventricular Dysfunction

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Heart Failure (HF) and valvular heart disease (VHD) are disproportionate problems in rural and remote Australia (RRA) relative to the rest of the country, due in part to an ageing rural population and to the frequency of rheumatic heart disease in the Aboriginal community. Late diagnoses can lead to avoidable hospital admissions and expense to the Australian health system. Echocardiography is the imaging investigation of choice, and a cornerstone of management, but access to this essential test shows huge geographic variations in Australia. The primary reason for this is the dependence of this technique on expert acquisition. Artificial intelligence (AI) has now been harnessed to optimise echocardiographic image acquisition, and secure, cloud-based storage enables remote measurement and interpretation. This trial seeks to demonstrate the effectiveness of AI-based echocardiography-guided triage and management of patients with known or suspected heart disease in RRA. This study will involve the conduct of a world-first randomised controlled trial of AI-testing and early intervention to detect early stages of HF and VHD, select appropriate management, reduce admissions and preserve functional status and quality of life. The study will be conducted with partipants in RRA, aged 40 years and older with at least one HF risk factor and recruited through clinic and community outreach in four sites with the involvement of remote outreach from i) Alice Springs Hospitals, ii) Nepean Hospital to Dubbo Hospital and Western NSW, iii) Princess Alexandra Hospital to Roma, Charleville and Western Queensland, and iv) Perth Aboriginal communities in partnership with the Royal Perth hospital and the Derbarl Yerrigan Health Service. Approximately 1200 individuals at risk for HF and VHD will be screened and followed up. The study will be conducted in partnership with Aboriginal community partners.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Heart Failure Valve Heart Disease

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Multicentre RCT comparing AI-TTE with usual care

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Outcome Assessors

PROBE design

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Screening/Management Plan

AI-guided echo acquisition performed using a desktop echo machine (uSmart 3300, Terason, Burlington, MA) with AI software (Caption Health, Brisbane, CA) obtained by a non-expert (eg. clinic nurse, registrar general practitioner or physician) at the remote clinic, usually on the day of clinic review. Images will then be uploaded onto a secure cloud and downloaded at the core lab for measurement, interpretation and reporting.

Group Type EXPERIMENTAL

AI-guided echo

Intervention Type DIAGNOSTIC_TEST

AI-guided echocardiography

Usual care

Standard echo acquisition by a sonographer either at a referral hospital, or by a visiting team - usually necessitating a delay. Measurement, interpretation and reporting will happen as usual.

Group Type ACTIVE_COMPARATOR

Standard echo

Intervention Type DIAGNOSTIC_TEST

Standard echocardiography

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

AI-guided echo

AI-guided echocardiography

Intervention Type DIAGNOSTIC_TEST

Standard echo

Standard echocardiography

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Age \>45 years
* eligible for Medicare
* exercise intolerance or cardiovascular (CV) risk factors

Exclusion Criteria

* Known HF or HVD
* situations where cardio-protection is already indicated (eg. known CAD)
* comorbid conditions with life expectancy \<2 years
* inability to provide written informed consent

Minimum Eligible Age

45 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No