Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool

<b>Introduction:</b> Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process. <b>Methodology:</b> Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. The transplants were randomly divided into training (70%) and validation (30%) sets. Death-censored graft survival was the primary performance indicator. We experimented with four machine learning (ML) models assessed for calibration and discrimination [integrated Brier score (IBS) and Harrell’s concordance index]. We assessed the potential clinical utility using decision curve analysis. <b>Results:</b> XGBoost demonstrated the best discriminative performance for survival (area under the curve = 0.73, 0.74, and 0.75 at 3, 7, and 10 years post-transplant, respectively). The concordance index was 0.72. The calibration process was adequate, as evidenced by the IBS score of 0.09. <b>Conclusion:</b> By evaluating possible donor–recipient pairs based on graft survival, the AI-based UK Live-Donor Kidney Transplant Outcome Prediction has the potential to enhance choices for the best live-donor selection. This methodology may improve the outcomes of kidney paired exchange schemes. In general terms we show how the new AI and ML tools can have a role in developing effective and equitable healthcare. 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.Differences in healthcare systems and data collection methodologies challenge the development of universally applicable predictive models for kidney transplantation outcomes.There is a recognized need for region-specific predictive models that take into account local healthcare practices and patient demographics to enhance transplant outcomes. 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. Differences in healthcare systems and data collection methodologies challenge the development of universally applicable predictive models for kidney transplantation outcomes. There is a recognized need for region-specific predictive models that take into account local healthcare practices and patient demographics to enhance transplant outcomes. The development of the UK-LTOP model using artificial intelligence to predict living-donor kidney transplant outcomes in the UK, showcasing superior discriminative performance and calibration compared to existing models.Insights into the challenges of creating a universal risk calculator for organ transplantation due to significant differences between healthcare systems in the USA and the UK.Evidence that machine learning techniques can effectively use regional transplant registry data to predict transplantation outcomes, potentially applicable across Europe. The development of the UK-LTOP model using artificial intelligence to predict living-donor kidney transplant outcomes in the UK, showcasing superior discriminative performance and calibration compared to existing models. Insights into the challenges of creating a universal risk calculator for organ transplantation due to significant differences between healthcare systems in the USA and the UK. Evidence that machine learning techniques can effectively use regional transplant registry data to predict transplantation outcomes, potentially applicable across Europe. The UK-LTOP model could significantly enhance pre-transplant decision-making by accurately predicting graft survival, thereby improving clinical outcomes for patients receiving living-donor kidney transplants.This study underscores the importance of region-specific predictive models in organ transplantation, potentially influencing policy toward adopting AI-based tools in healthcare systems.By demonstrating the effectiveness of AI in utilizing local data for outcome prediction, the study could encourage the development of similar models in other regions, improving global transplant success rates. The UK-LTOP model could significantly enhance pre-transplant decision-making by accurately predicting graft survival, thereby improving clinical outcomes for patients receiving living-donor kidney transplants. This study underscores the importance of region-specific predictive models in organ transplantation, potentially influencing policy toward adopting AI-based tools in healthcare systems. By demonstrating the effectiveness of AI in utilizing local data for outcome prediction, the study could encourage the development of similar models in other regions, improving global transplant success rates.