Title: Explainable machine learning models for clinical decision support in kidney transplant offering
Funder: Department of Health and Social Care

Abstract: Background The disparity between the size of the waiting list and the availability of donor kidneys means that clinicians often consider organs from suboptimal donors for transplantation. Such decisions can be challenging, and no decision support tools are currently available to help support clinicians and patients. Aims and objectives We aim to create a clinical decision support tool that will help a clinician and patient make a personalised decision about a given organ offer. This phase 1 project will train and validate machine learning (ML) models that accurately predict both graft and patient survival if a transplant goes ahead, as well as the outcome if the offer is declined and the patient waits for a subsequent offer. We will also undertake initial scoping work with patients and clinicians to determine the best way to present these predictions to patients and clinicians at the point of organ offer. Subsequent phases will develop the clinical decision tool based on these models and evaluate its use in clinical trials. Methods We will use data from the UK transplant registry to train ML models that predict post-transplant graft and patient survival, as well as patient outcomes if the offer is declined. These models will be validated in independent subsets of the data to assess performance and compared to existing regression-based approaches. We will undertake semi-structured interviews to explore optimum methods of data presentation in the proposed clinical decision tool. Timelines Phase 1 will take 12 months, with model development and user requirement scoping running in parallel. Impact and dissemination Development of a clinical decision aid will help to reduce disparity in decision making, improve informed consent and potentially improve organ utilisation. Results of the study will be disseminated via peer-reviewed journals, conferences and transplant organisations, and communicated in plain English to patient groups.

Lay abstract: Around 2,500 deceased donor kidney transplants are performed each year in the UK. At any time, there are around 5,000 patients on the kidney transplant waiting list. The shortage of organs available for transplant means that some patients become too unwell for surgery or die whilst waiting. Because of this, doctors often consider kidneys from donors who are less ideal due to age or other medical problems. These decisions are made by the transplant doctors based upon the information available at the time of offer. Details are rarely discussed with the patient. Doctors use their clinical experience, but do not have tools available to help them predict what would happen if they chose to accept or decline an offer and wait for the next one. Previous research suggests that it is often better to accept an offer rather than waiting longer for something better. In this project, we plan to use real-world data from twenty years of previous transplant offers and outcomes in the UK to train Artificial Intelligence (AI) models that will allow us to predict the outcome if an offer is accepted and transplanted or declined and wait for another offer. We plan to develop a web-based tool that presents this information in a simple fashion that both the doctors and patients can use when making a decision. We will then test this tool in a real-life setting to assess the impact on patients, clinicians and transplant outcomes. This application is for the funding for initial work on the project. It will allow the team to acquire the necessary data from NHS Blood and Transplant, check and clean the data, and create some initial AI models to make accurate predictions of transplant and patient survival. Once complete, we will then apply for further funding for further development and real-world testing. Initial work with patients at the Oxford Transplant Centre has shown that most patients would welcome more involvement in the decision to accept or decline an organ. During the project, patient input will help us to identify what information patients would find useful when deciding on an organ offer, and how this should be communicated. Patients will also be involved in pilot testing of the tool, so that we can get feedback and adapt it to meet their needs. We will ensure rapid sharing and take-up of our findings via a range of methods, including academic publications and conference presentations. We will also publish plain English summaries on the websites of our research teams and social media and communicate them to patient organisations. Ultimately, we hope to create a tool that we can make available to the 24 UK kidney transplant centres for clinical use.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: RENAL AND UROGENITAL