Abstract
Chronic liver disease (CLD) is an ignored epidemic. Premature mortality is considerable and in the United Kingdom (UK) liver disease is in the top three for inequitable healthcare alongside heart and respiratory disease. Fifty percentage of patients with CLD are first diagnosed with cirrhosis after an emergency presentation translating to poorer patient outcomes. Traditional models of care have been based in secondary care when the need is at community level. Investigating patients for disease based on their risk factors at a population level in the community will identify its presence early when there is potential reversibility. Innovation is needed in three broad areas to improve clinical care in this area: better access to diagnostics within the community, integrating diagnostics across primary and secondary care and utilizing digital healthcare to enhance patient care. In this article, we describe how the Integrated Diagnostics for Early Detection of Liver Disease (ID-LIVER) project, funded by UK Research and Innovation, is developing solutions in Greater Manchester to approach the issue of diagnosis of liver disease at a population level. The ambition is to build on innovative pathways previously established in Nottingham by bringing together NHS organizations, academic partners and commercial organizations. The motivation is to co-create and implement a commercial solution that integrates multimodal diagnostics via cutting edge data science to drive growth and disrupt the currently inadequate model. The ambitious vision is for this to be widely adopted for early diagnosis and stratification of liver disease at a population level within the NHS.
Highlights
Liver disease is a significant health burden worldwide and is recognized as a leading cause of mortality and morbidity in the United Kingdom (UK)
ID-LIVER: Diagnostics in Liver Disease risen by 400% since 1970, contrasting with improvements in mortality for other major diseases [2]
We identified three gaps which we believed would improve the identification of early liver disease
Summary
We aim to use machine-learning algorithms to integrate patient and diagnostic data from multiple sources to develop a model to detect patients at the highest risk of progression to clinically significant disease These individuals can be targeted for intervention to reduce this risk with the potential to improve health outcomes and costs. The Richards Report, part of the NHS Long Term plan focusing on “Diagnostics: Recovery and Renewal”, stated that “Effective pro-active management of patients at risk and at earlier stages of the [liver]disease course can improve outcomes for patients and lower costs for the NHS” [24] Establishing infrastructure, such as community diagnostic hubs, provide an opportunity for liver disease. This is in line with the government’s aim for both improving inequalities in healthcare and providing “proactive, predictive, and personalized prevention” in regards to long term morbidity and mortality [25]
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