Title: Clinical validation of the AI Clinician decision support system for sepsis treatment
Funder: Department of Health and Social Care

Abstract: Sepsis is life-threatening organ dysfunction due to severe infection and affects 250,000 patients annually in the UK, of whom 48,000 die. In addition, virtually all Covid-19 intensive care unit (ICU) deaths had sepsis. As part of ICU treatment, the cornerstone of sepsis resuscitation is the administration of intravenous fluids and/or vasopressors to maintain blood flow to prevent organ failure. However, uncertainty around correct dosing and timing of treatments, partially due to clinical syndrome heterogeneity, leads to poorer outcomes and increased ICU-resource use. We tackle this problem using an AI that learned from ICU electronic healthcare records to provide personalised treatment recommendations to clinicians. Our AI recommender system, the &ldquo;AI Clinician&rdquo;, extracted implicit knowledge from large ICU-databases and learned optimal sepsis treatment by analysing myriads of clinical decisions. We demonstrated in proof-of-principle retrospective studies that our AI s treatment strategy was on average better than clinicians: In a 100,000-patient validation cohort, patient mortality was lowest where clinicians actual doses matched AI recommendations: mortality rates rose, in a dose-dependent manner, as clinicians actual decisions diverged from AI recommendations. Thus, the 3 our technology s competitive advantages are that it can 1) extract relevant knowledge from our data sets, which are much larger than human clinicians could experience in a lifetime; 2) learn optimal strategies from suboptimal previous clinical decision examples; 3) optimise sequential decisions in real-time as patient conditions evolve. Our AI technology requires now prospective validation in relevant, operational environments - we propose this in four ICUs across two NHS trusts. Therefore, we will build a software platform capable of interfacing with many different NHS/ICU IT systems and running our AI algorithm in real-time, to provide dosing suggestions and their justifications to ICU physicians. We will protect intellectual property and follow the processes for medical device software regulation to allow certification and commercialisation.

Lay abstract: Sepsis is when the body s response to severe infection causes vital organs to stop working normally and is a worldwide healthcare challenge. It is a leading cause of death and the most expensive condition treated in hospitals. It was recognised as a top research priority by the James Lind Alliance, a partnership of patients and clinicians to prioritise the most pressing unanswered questions facing the NHS. A key part of the treatment of sepsis is the administration of intravenous fluids (sterile salty water given directly into the veins) and vasopressors (drugs that squeeze the blood vessels to increase blood pressure). However, there is huge uncertainty around the individual dosing of these drugs in an individual patient. A tool to personalise these medications could improve patient survival. We have developed a new method to automatically and continuously review and recommend the correct dose of these medications to doctors, which was created using artificial intelligence (AI) techniques applied to large medical databases. Our previous work has shown it has the potential to improve patient survival rates. Now, we intend to start clinical testing of this AI technology in the UK. First, we will develop a tool capable of processing patient data within the electronic patient record of NHS hospitals in real-time to suggest a course of action. We will start by evaluating and refining this tool in simulation studies. We will then test the AI tool in two NHS Trusts in a &ldquo;shadow mode&rdquo; when the result is not provided to the clinicians. This will allow comparison of actual decisions made and recommended decisions from the AI system. In the second stage of the clinical evaluation we will display the recommendations to clinicians to assess the acceptability of the tool to clinicians and also confirm the technical feasibility to inform future large scale clinical trials. We want this system to be used throughout the UK and elsewhere and so will investigate the best way to commercialise the tool, including the possibility of spinning out a company. The long-term expected benefits of this project are numerous: improved patient survival, reduced use of precious intensive care resources and reduction in healthcare costs. There is much debate around the perceptions of AI in medicine, the use of patient data in research and responsibilities in decision-making, so this project puts a strong emphasis on the involvement of the public, including patients, and clinicians. These stakeholders have been and will continue to be involved throughout. In order to influence clinical practice, we intend to feedback progress to our participating hospitals and to key executives within the Trusts. Wide dissemination to the scientific community, through peer-reviewed publication, and to the general public is also planned.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION;INFLAMMATORY AND IMMUNE SYSTEM