Prior models for predicting outcomes in organ transplantation have limited discriminative and calibration power, highlighting a need for improved prediction tools that can better guide clinical decision-making. Our primary aim was to build a machine learning model to predict deceased donor kidney transplant outcome using a large, multicenter set of data from the UK Transplant Registry (UKTR) and to assess the model's performance in comparison to the current benchmark UK-KDRI.
This study analyzed data from UKTR, including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of the machine learning models.
This study demonstrates the superiority of the UK-DTOP, an AI-enhanced predictive model, over traditional models like the KDRI, advocating for a shift toward advanced, data-driven tools in organ transplantation decision-making.
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