Lancet (London, England) · 2026
Triple cardiovascular disease detection with an artificial intelligence-enabled stethoscope (TRICORDER) in the UK: a cluster-randomised controlled implementation trial.
СтатьяРандомизированное исследованиеPragmatic Clinical TrialПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Kelshiker Mihir A, Bächtiger Patrik, Petri Camille F, Nakhare Saloni, Mansell Josephine, Chhatwal Karanjot, Alrumayh Abdullah, Zaman Jahed, Shah Moulesh, Young Holly, Roy Helena, Almonte Melanie T, Costelloe Céire, Razak Yasmin, Majeed Azeem, Howard James P, Barton Carys, Kramer Daniel B, Plymen Carla M, Peters Nicholas S
Аннотация
BACKGROUND: Early detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes.
METHODS: UK primary care practices were cluster randomised 1:1 to intervention (training and implementation in use of AI-stethoscopes in routine care) or control (routine care). Given the nature of the intervention, masking of participants (practices, clinicians, and patients) was not feasible. During cardiac examinations, the AI stethoscope recorded 15 s of single-lead electrocardiogram and phonocardiogram signals for input to three AI algorithms that returned binary predictions for the presence or absence of reduced left ventricular ejection fraction (≤40%), atrial fibrillation, and VHD (all with regulatory approval). The primary endpoint was incidence of any newly coded diagnosis of heart failure (all subtypes), expressed per 1000 patient-years (incidence rate ratio [IRR]), derived from a UK National Health Service Secure Data Environment. A coprimary endpoint stratified detection of heart failure by place of diagnosis (community-based vs via hospital admission). Secondary endpoints included atrial fibrillation and VHD detection rates, performance characteristics of the AI-stethoscope, use rates, and clinician-reported implementation barriers and enablers.
FINDINGS: Between Oct 30, 2023, and May 22, 2024, 205 practices were randomly assigned (96 to the intervention arm [701 933 registered patients] and 109 to the control arm [851 242 registered patients]). Intervention practices recorded 12 725 patient examinations with the AI-stethoscope, across 972 clinical users. Intention-to-treat analysis found heart failure detection did not differ between groups (IRR 0·94 [95% CI 0·86-1·02]); with no difference in community-based or hospital-based diagnoses (p>0·05).
INTERPRETATION: Implementation of an AI stethoscope in routine primary care did not significantly increase detection of heart failure or increase community-based diagnosis after 12 months of implementation. AI stethoscope use was independently associated with significantly higher detection rates of heart failure, as well as atrial fibrillation and VHD. This randomised controlled implementation trial establishes a pragmatic design with randomisation that generates real-world data essential for understanding and overcoming the barriers to implementation of innovation in health care.
FUNDING: National Institute for Health and Care Research, British Heart Foundation, and Imperial Health Charity.
METHODS: UK primary care practices were cluster randomised 1:1 to intervention (training and implementation in use of AI-stethoscopes in routine care) or control (routine care). Given the nature of the intervention, masking of participants (practices, clinicians, and patients) was not feasible. During cardiac examinations, the AI stethoscope recorded 15 s of single-lead electrocardiogram and phonocardiogram signals for input to three AI algorithms that returned binary predictions for the presence or absence of reduced left ventricular ejection fraction (≤40%), atrial fibrillation, and VHD (all with regulatory approval). The primary endpoint was incidence of any newly coded diagnosis of heart failure (all subtypes), expressed per 1000 patient-years (incidence rate ratio [IRR]), derived from a UK National Health Service Secure Data Environment. A coprimary endpoint stratified detection of heart failure by place of diagnosis (community-based vs via hospital admission). Secondary endpoints included atrial fibrillation and VHD detection rates, performance characteristics of the AI-stethoscope, use rates, and clinician-reported implementation barriers and enablers.
FINDINGS: Between Oct 30, 2023, and May 22, 2024, 205 practices were randomly assigned (96 to the intervention arm [701 933 registered patients] and 109 to the control arm [851 242 registered patients]). Intervention practices recorded 12 725 patient examinations with the AI-stethoscope, across 972 clinical users. Intention-to-treat analysis found heart failure detection did not differ between groups (IRR 0·94 [95% CI 0·86-1·02]); with no difference in community-based or hospital-based diagnoses (p>0·05).
INTERPRETATION: Implementation of an AI stethoscope in routine primary care did not significantly increase detection of heart failure or increase community-based diagnosis after 12 months of implementation. AI stethoscope use was independently associated with significantly higher detection rates of heart failure, as well as atrial fibrillation and VHD. This randomised controlled implementation trial establishes a pragmatic design with randomisation that generates real-world data essential for understanding and overcoming the barriers to implementation of innovation in health care.
FUNDING: National Institute for Health and Care Research, British Heart Foundation, and Imperial Health Charity.