Clinical trial NCT05987670: Triple Cardiovascular Disease Detection With an Artificial Intelligence-enabled Stethoscope
Official title: Triple Cardiovascular Disease Detection With an Artificial Intelligence-enabled Stethoscope
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Heart Failure
Heart Valve Diseases
Atrial Fibrillation
Heart Murmurs
Congestive Heart Failure
Heart Failure With Reduced Ejection Fraction
Keywords: cluster randomized controlled trial
artificial intelligence
digital health
cardiology
primary care
Lead sponsor: Imperial College London
Sponsor class: OTHER
Collaborators: name: Imperial College Health Partners
First posted: 2023-08-14
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Open label cluster randomised controlled trial
Primary purpose: DIAGNOSTIC

Brief summary: Heart failure (HF) is a condition in which the heart cannot pump blood adequately. It is increasingly common, consumes 4% of the UK National Health Service (NHS) budget and is deadlier than most cancers. Early diagnosis and treatment of HF improves quality of life and survival. Unacceptably, 80% of patients have their HF diagnosed only when very unwell, requiring an emergency hospital admission, with worse survival and higher treatment costs to the NHS. This is largely because General Practitioners (GPs) have no easy-to-use tools to check for suspected HF, with patients having to rely on a long and rarely completed diagnostic pathway involving blood tests and hospital assessment.

The investigators have previously demonstrated that an artificial intelligence-enabled stethoscope (AI-stethoscope) can detect HF in 15 seconds with 92% accuracy (regardless of age, gender or ethnicity) - even before patients develop symptoms. While the GP uses the stethoscope, it records the heart sounds and electrical activity, and uses inbuilt artificial intelligence to detect HF.

The goal of this clinical trial is to determine the clinical and cost-effectiveness of providing primary care teams with the AI-stethoscope for the detection of heart failure. The main questions it aims to answer are if provision of the AI-stethoscope:

1. Increases overall detection of heart failure
2. Reduces the proportion of patients being diagnosed with heart failure following an emergency hospital admission
3. Reduces healthcare system costs

200 primary care practices across North West London and North Wales, UK, will be recruited to a cluster randomised controlled trial, meaning half of the primary care practices will be randomly assigned to have AI-stethoscopes for use in direct clinical care, and half will not. Researchers will compare clinical and cost outcomes between the groups.

Detailed description: Triple Cardiovascular Disease Detection with Artificial Intelligence-enabled Stethoscope (TRICORDER) is an open label, cluster randomised controlled trial. The aim is to determine whether use of an artificial intelligence-enabled stethoscope (AI-stethoscope) in UK Primary Care improves community-based detection of heart failure (HF), compared with usual care. 200 primary care practices in North West London (UK) will be randomised to receive the AI-stethoscope (intervention arm) or continue with usual care (control arm). The intervention arm will use the AI-stethoscope in routine clinical practice. Outcomes will be measured using pooled primary and secondary care clinical and cost-data, as well as clinician questionnaires.

Eligibility criteria: Inclusion Criteria:

* Primary care practices that care for adult patients and have the ability to request natriuretic peptide blood testing
* Primary care practices within the NIHR North West London Clinical Research Network or Betsi Cadwaladr University Health Board.

Exclusion Criteria:

* Poor WiFi and/or mobile data connectivity within primary care consulting rooms
* No face-to-face patient consultations

Primary outcomes:
measure: Incidence of heart failure (co-primary)
timeFrame: 24 months
measure: Ratio of route to diagnosis of heart failure (co-primary) between emergency and community-based pathways
timeFrame: 24 months
Secondary outcomes:
measure: Incidence of atrial fibrillation
timeFrame: 24 months
measure: Incidence of valvular heart disease
timeFrame: 24 months
measure: Cost-consequence (AF)
timeFrame: 24 months
measure: Cost-consequence (HFrEF)
timeFrame: 24 months
measure: Cost-consequence (VHD)
timeFrame: 24 months
measure: Health service utilisation
timeFrame: 24 months
measure: Proportion of patients prescribed guideline-directed medical therapy
timeFrame: 24 months
measure: Device therapy
timeFrame: 24 months
measure: Uptake and utilisation
timeFrame: 24 months
measure: Determinants of uptake and utilisation
timeFrame: 24 months
measure: Patient quality of life
timeFrame: 24 months
Other outcomes:
measure: Sensitivity analysis
timeFrame: 24 months
Locations:
city: London
country: United Kingdom
facility: NHS North West London Integrated Care System
Study officials:
affiliation: Imperial College London
name: Nicholas S Peters, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Bachtiger P, Petri CF, Scott FE, Ri Park S, Kelshiker MA, Sahemey HK, Dumea B, Alquero R, Padam PS, Hatrick IR, Ali A, Ribeiro M, Cheung WS, Bual N, Rana B, Shun-Shin M, Kramer DB, Fragoyannis A, Keene D, Plymen CM, Peters NS. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health. 2022 Feb;4(2):e117-e125. doi: 10.1016/S2589-7500(21)00256-9. Epub 2022 Jan 5.
citation: Bachtiger P, Kelshiker MA, Petri CF, Gandhi M, Shah M, Kamalati T, Khan SA, Hooper G, Stephens J, Alrumayh A, Barton C, Kramer DB, Plymen CM, Peters NS. Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis. BMJ Health Care Inform. 2023 Mar;30(1):e100718. doi: 10.1136/bmjhci-2022-100718.