| Autor: |
Ali H; University Hospitals of Coventry and Warwickshire, Coventry, UK.; Research Centre for Health and Life Sciences, Coventry University, Coventry, UK., Shroff A; ITU/WHO Focus Group on AI for Health CTO, Medindia.net, Co-founder, Xtend.AI, MOHAN Foundation, Chennai, India., Soliman K; Department of Medicine, Division of Nephrology, Medical University Hospitals of South Carolina, Charleston, SC, USA.; Medicine Services, Ralph H. Johnson VA Medical Center, Charleston, SC, USA., Molnar MZ; Department of Internal Medicine, Division of Nephrology & Hypertension, University of Utah, Spencer Fox Eccles School of Medicine, Salt Lake City, UT, USA., Sharif A; University Hospitals of Birmingham, Birmingham, UK., Burke B; Research Centre for Health and Life Sciences, Coventry University, Coventry, UK., Shroff S; ITU/WHO Focus Group on AI for Health CTO, Medindia.net, Co-founder, Xtend.AI, MOHAN Foundation, Chennai, India., Briggs D; Histocompatibility and Immunogenetics Laboratory, Birmingham Centre, NHS Blood and Transplant, Birmingham, UK.; Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK., Krishnan N; University Hospitals of Coventry and Warwickshire, Coventry, UK.; Institute of Cardio-metabolic Medicine, University Hospitals of Coventry and Warwickshire, Coventry, UK.; Clinical Health, Centre of Health and Community Care, Institute of Health and Wellbeing, Coventry University, Coventry, UK. |
| Abstrakt: |
The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.74 and an area under the curve (AUC) consistently above 0.73, indicating robust discriminative ability and calibration. In comparison to the traditional Kidney Donor Risk Index (KDRI), which achieved a lower concordance index of 0.57, the UK-DTOP model marked a significant improvement, underscoring its superior predictive accuracy. The advanced capabilities of the XGBoost model were further highlighted through calibration assessments using the Integrated Brier Score (IBS), showing a score of 0.14, indicative of precise survival probability predictions. Additionally, unsupervised learning via k-means clustering was employed to identify five distinct clusters based on donor and transplant characteristics, uncovering nuanced insights into graft survival outcomes. These clusters were further analyzed using Bayesian Cox regression, which confirmed significant survival outcome variations across the clusters, thereby validating the model's effectiveness in enhancing risk stratification. The UK-DTOP tool offers a comprehensive decision-support system that significantly refines pre-transplant decision-making. The study's findings advocate for the adoption of AI-enhanced tools in healthcare systems to optimize organ matching and transplant success, potentially guiding future developments in global transplant practices. |
