Title: Development of machine learning based clinical decision support tool for improving care in acute upper gastrointestinal bleeding
Funder: Department of Health and Social Care

Abstract: Background: Risk stratification scores for acute upper gastrointestinal bleeding (AUGIB) have limitations. Many of the currently available risk-scores have been developed using limited data at specific (often only one) time-points, do not predict accurately and are not developed robustly enough to be used in clinical practice. Machine learning (ML) models detect, and model complex patterns using multiple variables from patient's medical information collected over different time-points, and have the potential to improve the predictive value of currently available clinical risk-scores.Aim: To develop and test a decision support tool (DST) by applying ML on country-wide and electronic patient record (ePR) data of adult patients presenting with AUGIB to support clinical management including transfusion practices and improve outcomes. Objectives:(i) Develop ML-based models for predicting risk of adverse health outcomes in patients suspected of AUGIB, using: Cross-sectional data from UK wide national audits (2007 and 2022 UK national AUGIB audit & re-audit, respectively) ePR data from Oxford University Hospitals Foundation Trust (OUHFT) for a detailed analysis of anonymised data (ii) Compare performance of the developed ML-based models to established clinical risk-scores (Glasgow-Blatchford, Rockall, ABC etc.)(iii) Externally validate the developed ML-based models on data from allied NHS hospitals, with data-sharing agreements(iv) Develop DST software using the validated ML-based models to assist individualised care plans for patients with AUGIB(v) Assess the DST at point-of-care Methods: 1. A prognostic modelling study to develop ML-based models for prediction of primary and secondary outcomes Primary outcome: composite outcome of need for hospital-based intervention or risk of dying Secondary outcomes: predicting early (5d) and late (30d) risk of dying; prediction of need for specific interventions, risk of rebleeding, length of stay and comparative performance of ML-based models against established clinical risk-scores 2. External validation of the ML-based models in allied NHS Trusts using ePR3. Validated ML-based models will be used to develop a clinical DST in collaboration with Sensyne Health, a small/medium sized enterprise specialising in the field4. DST will be assessed at OUHFT by linking it to clinical 'group & save' requests for patients with AUGIB, through an impact study to evaluate the feasibility and accuracy of the DST in routine clinical practice Timelines for delivery: Over 36 months: 6 months - compilation of national audit and ePR datasets; 16 months -h; Validation of ML-based models; 20 months -h; development of DST and linking to group & save requests; 30 months -h; completion of impact study.Anticipated impact and dissemination: A valid, tested DST available at the point-of-care, will improve day-to-day management of AUGIB, a common medical emergency requiring multi-disciplinary management. It will encourage efficient use of blood products, support early discharge decisions of low-risk patients, provide real-time prognostication for acutely unwell patients, and aid early identification of patients requiring specific interventions. A validated, practical, easy to use DST will be of interest to hospitals across the UK and centres abroad. Dissemination will be via scientific presentations, open-access publications in scientific journals and public engagement events, with help from patient representatives.

Lay abstract: Background: Acute upper gastrointestinal bleeding (AUGIB) is sudden bleeding from the food-pipe, stomach or first part of the small bowel. It is a common medical emergency with over 50,000 UK hospital admissions every year. Patients may vomit blood, have black coloured faeces, or feel dizzy from blood loss. Its severity varies from mild (needing outpatient treatment) to severe (life-threatening, needing urgent hospital admission). The treatment involves different medical specialists and includes medications, drips to provide fluid, blood transfusions, and procedures using tubes with cameras inserted through the mouth or an operation to identify the source of the bleeding and to control it. The risk of dying from AUGIB in the UK was 10% in 2007, being 7% for emergency admissions and 26% when it occurred in patients already in hospital. However, it is challenging to identify patients at risk of dying or prioritise patients for urgent treatment. It is also difficult to know when bleeding will stop on its own, continue, or happen again. Risk-scores have been developed to assist clinicians in making these decisions, but are not sufficiently good to be used widely.Computers now have the potential to process very large amounts of medical information and to identify common patterns. One method of doing this is machine learning (ML). We propose to use ML to improve accuracy of risk-scores to predict health outcomes of patients with AUGIB. Aim: To develop and test the impact of a computer software package using ML-based risk-scores to help clinicians make accurate and timely clinical decisions when managing patients with suspected AUGIB. Design and methods: We will use many different datasets that include unidentifiable medical information on patients with suspected AUGIB. The first dataset is from a UK national audit done in 2007. The second dataset will be from an update to the 2007 audit that will be completed in 2022. The third and fourth datasets will be extracted from electronic patient health records at Oxford University Hospital Foundation Trust Foundation Trust (OUHFT) and other NHS hospitals, respectively. I will use the first three datasets to develop ML-based models to identify AUGIB patients as low or high risk of an adverse health outcome. Low risk patients could be safely discharged from hospital, whilst high risk patients would require hospital admission for further examination and treatment. I will also use ML-based models to help identify need for specific treatment options and to estimate the length of hospital stay for those being admitted to hospital. Once the risk-scores have been developed, I will assess how well they work using 'new' data from other NHS hospitals (fourth dataset). I will then present the risk-scores in a computer package so that clinicians can easily use them to aid their clinical decision making. This computer package will be added to the computer systems at OUHFT and I will assess how well it works in routine clinical practice. Patient and public involvement: Two patients, who have experienced AUGIB, have seen and commented on the research proposal. Both of them have kindly agreed to advise at all stages of this project. We plan to meet with them twice a year to discuss progress. The members will also help publicise the findings of the study. Appropriate training will be provided to the patient representatives to help contribute to the project.Dissemination: Results will be disseminated as written and audio-visual summaries, completed with contributions from patient representatives. Results will be published in scientific journals, presented at scientific meetings and at public engagement events to discuss the findings.

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