From the University Hospitals of Coventry and Warwickshire, United Kingdom.
University Hospitals of Mississippi.
ASAIO J. 2024 Sep 1;70(9):808-818. doi: 10.1097/MAT.0000000000002190. Epub 2024 Mar 29.
In kidney transplantation, pairing recipients with the highest longevity with low-risk allografts to optimize graft-donor survival is a complex challenge. Current risk prediction models exhibit limited discriminative and calibration capabilities and have not been compared to modern decision-assisting tools. We aimed to develop a highly accurate risk-stratification index using artificial intelligence (AI) techniques. Using data from the UNOS database (156,749 deceased kidney transplants, 2007-2021), we randomly divided transplants into training (80%) and validation (20%) sets. The primary measure was death-censored graft survival. Four machine learning models were assessed for calibration (integrated Brier score [IBS]) and discrimination (time-dependent concordance [CTD] index), compared with existing models. We conducted decision curve analysis and external validation using UK Transplant data. The Deep Cox mixture model showed the best discriminative performance (area under the curve [AUC] = 0.66, 0.67, and 0.68 at 6, 9, and 12 years post-transplant), with CTD at 0.66. Calibration was adequate (IBS = 0.12), while the kidney donor profile index (KDPI) model had lower CTD (0.59) and AUC (0.60). AI-based D-TOP outperformed the KDPI in evaluating transplant pairs based on graft survival, potentially enhancing deceased donor selection. Advanced computing is poised to influence kidney allocation schemes.
在肾移植中，将受者与低风险供体配对以优化移植物-供体存活率是一个复杂的挑战。目前的风险预测模型表现出有限的判别和校准能力，并且尚未与现代决策辅助工具进行比较。我们旨在使用人工智能 (AI) 技术开发一个高度准确的风险分层指数。使用 UNOS 数据库（156749 例已故肾移植，2007-2021 年）的数据，我们将移植随机分为训练（80%）和验证（20%）集。主要测量指标是死亡风险校正移植物存活率。评估了四种机器学习模型的校准（综合 Brier 评分 [IBS]）和判别（时间依赖性一致性 [CTD] 指数），并与现有模型进行了比较。我们使用英国移植数据进行了决策曲线分析和外部验证。Deep Cox 混合模型表现出最佳的判别性能（移植后 6、9 和 12 年的曲线下面积 [AUC] 分别为 0.66、0.67 和 0.68），而 CTD 为 0.66。校准情况尚可（IBS = 0.12），而 KDPI 模型的 CTD（0.59）和 AUC（0.60）较低。基于 D-TOP 的 AI 模型在评估基于移植物存活率的移植对方面优于 KDPI，这可能会增强已故供体的选择。先进的计算技术有望影响肾脏分配方案。
