布里氏评分
判别式
医学
一致性
肾移植
接收机工作特性
肾移植
人工智能
机器学习
内科学
肾
计算机科学
数据挖掘
作者
Hatem Kaies Ibrahim Elsayed Ali,Mahmoud Mohamed,Miklos Z. Molnar,Tibor Fülöp,B. F. Burke,Anjali Shroff,Sunil Shroff,David Briggs,Nithya Krishnan
出处
期刊:Asaio Journal
[Ovid Technologies (Wolters Kluwer)]
日期:2024-03-28
标识
DOI:10.1097/mat.0000000000002190
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI