Explainable machine learning models for clinical decision support in kidney transplant offering

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 on the information available at the time of offer. Details are rarely discussed with the patient. Doctors use their clinical experience, but do not have the 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 wait for something better.

This project will use real-world data from 20 years of previous transplant offers and outcomes in the UK to train artificial intelligence (AI) models – that will allow prediction of the outcome if an offer is accepted and transplanted or declined, to wait for another offer.  A web-based tool will be developed and tested to present information in a simple format that both the doctors and patients can use when making their decisions.

This project is led by the University of Oxford. Helena Webb, Horizon Transitional Assistant Professor will supervise the design and conduct semi-structured interviews as part of this project.

Funded by: AI in Health Care Award – this project is one of 38 pioneering AI projects supporting revolutionising care and accelerating diagnosis.

Outputs: Clinical decision support systems used in transplantation: are they tools for success or an unnecessary gadget, Transplantation, 108(1):p 72-99, January 2024. | DOI: 10.1097/TP.0000000000004627

Project duration: 1^st November 2021 – 30 October 2022