인공지능 청진기로 3중 심혈관 질환 감지 (TRICORDER)
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:
- Increases overall detection of heart failure
- Reduces the proportion of patients being diagnosed with heart failure following an emergency hospital admission
- 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.
연구 개요
상세 설명
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.
연구 유형
등록 (실제)
단계
- 해당 없음
연락처 및 위치
연구 장소
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London, 영국
- NHS North West London Integrated Care System
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참여기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
설명
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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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간섭 없음: Control
Usual care |
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실험적: Intervention
Receive 3-6 AI-stethoscopes (Eko DUO, Eko Health Inc, CA, USA) including artificial intelligence software for detection of:
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Clinicians at practices in the intervention arm will be provided with one session of in-person training in use of the AI-stethoscope within 2 weeks of randomisation, including
다른 이름들:
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Incidence of heart failure (co-primary)
기간: 24 months
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Difference in incidence of coded new diagnoses of heart failure (HF) |
24 months
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Ratio of route to diagnosis of heart failure (co-primary) between emergency and community-based pathways
기간: 24 months
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Difference in ratio of the incidence of coded diagnoses of HF via hospital admission-based versus community-based pathways. |
24 months
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Incidence of atrial fibrillation
기간: 24 months
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New coded diagnoses of atrial fibrillation (AF) |
24 months
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Incidence of valvular heart disease
기간: 24 months
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New coded diagnoses of valvular heart disease (VHD) |
24 months
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Cost-consequence (AF)
기간: 24 months
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Cost-consequence analysis (form of health economic evaluation) for diagnosis of atrial fibrillation, stratified by route to diagnosis. Presented in pounds sterling. |
24 months
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Cost-consequence (HFrEF)
기간: 24 months
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Cost-consequence analysis (form of health economic evaluation) for diagnosis of HFrEF, stratified by route to diagnosis. Presented in pounds sterling. |
24 months
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Cost-consequence (VHD)
기간: 24 months
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Cost-consequence analysis (form of health economic evaluation) for diagnosis of VHD, stratified by route to diagnosis. Presented in pounds sterling. |
24 months
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Health service utilisation
기간: 24 months
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Health service utilisation for diagnostics e.g. rates of request for echocardiography, electrocardiography, primary care appointments. Collected from NHS organisation business intelligence repositories and UK Trusted Research Environments. |
24 months
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Proportion of patients prescribed guideline-directed medical therapy
기간: 24 months
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Proportion of patients prescribedguideline-directed medical therapy (HF, AF, VHD) |
24 months
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Device therapy
기간: 24 months
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New implantation of cardiac resynchronisation therapy (CRT) and/or implantable cardioverter-defibrillator (ICD) |
24 months
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Uptake and utilisation
기간: 24 months
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Differential rates of uptake and utilisation of AI-stethoscope in primary care |
24 months
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Determinants of uptake and utilisation
기간: 24 months
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Determinants of utilisation of AI-stethoscope in primary care (clinician questionnaires) |
24 months
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Patient quality of life
기간: 24 months
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Healthy Days at Home (patient-level analysis) |
24 months
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기타 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Sensitivity analysis
기간: 24 months
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Patient-level sensitivity analyses will be performed for patients with abnormal Eko DUO predictions for HF, to identify direct associations between AI-stethoscope predictions and specific diagnostic codes for HF, AF and VHD |
24 months
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공동 작업자 및 조사자
수사관
- 수석 연구원: Nicholas S Peters, MD, Imperial College London
간행물 및 유용한 링크
일반 간행물
- 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.
- 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.
- Kelshiker MA, Bachtiger P, Mansell J, Kramer DB, Nakhare S, Almonte MT, Alrumayh A, Petri CF, Peters A, Costelloe C, Falaschetti E, Barton C, Al-Lamee R, Majeed A, Plymen CM, Peters NS. Triple cardiovascular disease detection with an artificial intelligence-enabled stethoscope (TRICORDER): design and rationale for a decentralised, real-world cluster-randomised controlled trial and implementation study. BMJ Open. 2025 May 21;15(5):e098030. doi: 10.1136/bmjopen-2024-098030.
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- 22HH8045
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