机器学习渐进性CKD风险预测模型与CKD矿物骨障碍相关
Joseph Aoki1, Omar Khalid1, Cihan Kaya1
1Sonic Healthcare USA, 12357A - A Riata Trace Parkway, Suite 210, Austin, TX 78727, USA.
Bone reports
|July 29, 2024
概括
机器学习准确地预测慢性病 (CKD) 的进展,并识别相关的CKD-矿物骨疾病 (CKD-MBD). 这项研究强调了CKD-MBD的不足测试,并将预测分析与早期发现这种并发症联系起来.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 生物化学 生物化学
- 人工智能的人工智能
背景情况:
- 慢性病 (CKD) 的进展是一个重大的健康问题.
- 慢性结核病-矿物质骨疾病 (CKD-MBD) 是一种严重的,未被认可的并发症.
- 对于CKD进展和CKD-MBD的预测模型之间的关联尚未得到充分理解.
研究的目的:
- 评估CKD-MBD的实验室测试利用情况.
- 评估基于机器学习的CKD风险分类器 (PCRC) 与CKD-MBD之间的关联.
- 调查预测分析在识别CKD-MBD中的实用性.
主要方法:
- 这是一项5年间对330,238名美国门诊患者进行的回顾性队列研究.
- 对eGFR,UACR,PTH,和酸盐的实验室测试数据的分析.
- 根据CKD-MBD的生物化学标志物对PCRC分类的评估.
主要成果:
- 观察到UACR,酸盐和PTH测试的使用不足.
- PCRC预测的CKD进展与酸盐和PTH水平的增加有关 (P < 0.01).
- 这些生化变化与CKD-MBD一致.
结论:
- 实验室检查CKD-MBD的实验室测试的利用率明显不足.
- PCRC预测的CKD进展与CKD-MBD有关,可能是在临床表现之前几年.
- 这项研究是首次将CKD进展与CKD-MBD的预测分析联系起来,为改善风险分层和治疗提供了潜力.
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