Title: Point-of-care Heart Failure Diagnosis for GP Use: Implementation and Evaluation of a Simple AI-tool into the Folio of Care Pathways Serviced by Imperial’s Connected Care National GP Network
Funder: Department of Health and Social Care

Abstract: This first real-world evaluation of a simple-to-use AI-tool for GP point-of-care diagnosis of heart failure (HF) builds on the track record of the Connected Care Bureau (CCB) in establishing impactful AI-enhanced pathways across a growing Connected Care GP network. Consuming 2% of the entire NHS budget, 1M people in the UK have HF &ndash; increasing 50% by 2040. Although 40% of patients will have seen their GP with symptoms of HF, the high demand, the importance of accuracy, and its difficulty results in diagnosis per NICE guidelines in only 4% of patients. Consequently, 80% of HF is diagnosed only at life-threatening hospital admission, with 83% found to have the common form with reduced ejection fraction (HFrEF). The Eko DUO digitally-enhanced stethoscope and cloud-based convolutional neural network is capable of determining if a patient has clinically significant HFrEF, at the national guideline-indicated treatment threshold of 40% EF with 100% sensitivity and 80% specificity. The project will apply the Eko DUO in easily implemented point-of-care diagnostic pathways over 12 months in 10 GP practices across two UK regions in 200 patients in whom the GP considers HFrEF as a possibility. All patients will also proceed through the established NHS pathway, with a repeat Eko DUO measurement performed for both direct correlation and reproducibility at the time of echocardiography - supplemented by real-world correlation with 300 unselected consecutive echos to explore refinements to diagnostic yield. Accuracy of the Eko DUO, and the time to diagnosis and resource utilisation of the two pathways will be compared. The twin challenges of early diagnosis of HF and its undiagnosed prevalence in the general population are among the biggest facing the NHS and would be transformed by an effective point-of-care diagnostic and pathway, anticipating our Phase 4 and 5 applications to the AI Award towards adoption nationally.

Lay abstract: Heart Failure (HF) is a devastating clinical condition in which the heart cannot pump blood effectively. It is common, has poor outcomes, and consumes more than 2% of NHS budget.1 Early diagnosis is key to starting treatment and improving outcomes, but currently, diagnosis is complex and too slow, with no recommended point-of-care tests available to GPs. Imperial College & NHS Trust s Connected Care Bureau (CCB) has established an increasing number of innovative care pathways that use AI-powered connected technologies, providing better and more cost-effective care across a growing GP network. Attracted by this track record, academic and commercial partnership has led to this proposal for the first real-world implementation and evaluation of a simple-to-use AI-tool for GP point-of-care diagnosis of HF integrated into the folio of CCB care pathways. Combining the technology and CCB s NHS pathway-first approach to implementation is the strength of this proposal. The Eko DUO is a &quot;smart&quot; stethoscope that simultaneously records an electrocardiogram (ECG -- measuring the heart s electrical activity) with heart sounds. The device is used like a standard stethoscope &ndash; placed on the chest for 15 seconds. Attractive as a tool that is familiar in GP workflow, it not only provides enhanced diagnostics from the ECG and heart sounds - of help to the GP - but most importantly provides immediate diagnosis of HF using an AI algorithm which has learned from 45,000 HF ECGs what the ECG of a HF patient looks like.2 Eko has adapted this algorithm for point-of-care diagnosis which we will evaluate in 500 patients in GP and secondary care by comparison with current NHS guideline-driven HF care pathways and diagnostic testing to measure accuracy and ability to transform HF care. The CCB has already implemented other clinical pathways that use AI-powered technologies for remote diagnosis and management, including for palpitations. CCB serves GPs and community cardiology in several UK regions, providing effective and labour-saving support and helping meet challenges in primary care. The CCB principles provide the ideal underpinning for the present proposal - starting with pathway design, and incorporating technology only where necessary as seamlessly and as automated as possible. Co-designed with patients and integrated with our shared patient-facing electronic health record, this approach to incorporation into a CCB pathway enables the DUO result to trigger both immediate NHS management guidance to the GP user, and booking the patient into specialist care pathways. This first real-world evaluation of Eko DUO for AI-driven diagnosis in HF will inform our intended submissions for Phases 4 and then 5 of this AI in Health Award to drive health system adoption locally and then nationally based on better outcomes, quality of life and more cost-effective care.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR