Title: Digital Therapeutic Management of Decompensated Cirrhosis (CirrhoCare): Clinical outcome prediction model for detecting decompensation events thus improving patient outcomes, personalising interventions, and preventing hospital re-admissions
Funder: Department of Health and Social Care

Abstract: Research Question: Is it feasible to accurately predict the clinical outcomes of decompensated events occurring in decompensated cirrhosis patients using AI models? If so can clinical accuracy be over 90% compared with traditional statistical models at 70% Background : Our recent clinical study funded by Innovate UK showed that over 50% of readmissions resulting from cirrhosis decompensation events are preventable. Our proposed technology CirrhoCare, with a set of AI algorithms (CirrhoX) aims to predict the occurrence of decompensation events and determine clinical prognosis to assist clinicians in determining the course of care pathway. Aims and objectives Primary objective is to establish the feasibility of using AI model to predict the occurrence of decompensation events in cirrhosis patients. Secondary objective is to determine whether the clinical accuracy can be over 90% when compared with traditional methods. Workplan and methods The workplan and methods employed include: Building a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index establish the clinical efficacy by validating the AI model predictions with the clinician predicted outcomes and real-world outcomes of the patients using the data we have recently collected. Time lines for delivery and outputs End to end project will be complete in 8 months. Planned outputs include : Predictive algorithm and its clinical therapeutic signposting trained AI/ML decompensation cirrhosis prediction model validated initial clinical efficacy and safety data Anticipated impact and dissemination CirrhoCare’s novel, multimodal, monitoring system with integrated clinical outcome AI-prediction model for detecting decompensation events in patients with advanced cirrhosis is feasible; enabling the e arly diagnosis of decompensating events through assisted intelligence and their intervention; hospital admissions will be fewer and shorter in duration We anticipate Cirrhocare with integrated CirrhoX prediction algorithm as a tool for managing cirrhosis patients at-risk of acute decompensation, at home.

Lay abstract: The British Liver Trust indicates deaths from liver disease have increased by 400% since 1970 and are now the third cause of premature death in working-age people, with 62,000 years of working life lost/year, and costing the NHS >&pound;3.5bn and &pound;7.3bn lost in productivity. With progressive liver scarring (cirrhosis), the only curative option is liver transplantation. However, this is unavailable to most patients, due to lack of donor organs. Cirrhosis patients can present acutely with liver deterioration, termed decompensation , from events like infection, requiring hospital admission and accounting for 60% of cirrhosis healthcare costs. Many require intensive care for their failing organs but with little indication of who might benefit, with mortality ranging from 10% with no organ impairment to 100% when multiple organs like the kidney and brain fail. In a recent Birmingham meeting with patient representation discussing cirrhosis decompensation, lack of guidance on prognosis was deemed a major cause of distress for patients and carers and such input helped galvanise this project. Currently, prognosis is determined by statistical clinical scores that are at best 70% accurate. They poorly capture who needs early transfer to intensive care for organ support or even referral to specialist centres. Thus, many patients do not receive the correct level of support, whilst others remain on intensive therapies with limited chance of success, with high costs incurred through protracted hospital admissions. There is an urgent need for early prediction of patient clinical outcome, to deliver the appropriate treatment for an individual s severity of cirrhosis decompensation, and to facilitate early hospital discharge for those with good prognosis. We propose to develop and test a novel clinical prediction model and algorithm for decompensated cirrhosis, using artificial intelligence (AI) techniques, to derive better prognostic tools to guide clinical decisions. The main aim of the project is to assess the ability of AI to increase prognostic accuracy to over 90%. Our algorithm can help guide clinicians as to which patients need hospitalisation and intensive care organ support, or conversely, whom to de-escalate intensive therapy. At later stages, this algorithm can be integrated with Electronic patient records, by providing real-time feedback on changing clinical status of patients on web/smart-phone apps for trained clinical staff. At every developmental stage, we will involve patients and public to ensure our research is clear, acceptable and useful. This will ensure the most effective and timely treatment for decompensated cirrhosis patients, at the right location, but also reduce costs from ineffective care and through more efficient hospital discharge planning. The Lancet commission report in 2017, indicated health and care system costs of &pound;1.8bn on inpatient/day stays. If we project to reduce 10% of admission lengths through our innovation, this could save the NHS &pound;180m.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: ORAL AND GASTROINTESTINAL