机器学习预测老年MetS患者的慢性病使用NHANES 2011-2020数据
Rejuvenation research
|December 10, 2025
概括
使用尿酸与高密度脂蛋白胆固醇 (UHR) 的比率,年龄,尿酸和血液尿素 (BUN) 的新风险模型准确地预测了代谢综合征的老年人慢性病 (CKD).
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 老年病的医生 老年病的医生
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 患病率在患有代谢综合征的老年人中正在上升.
- 在这个人群中,对CKD的早期发现和风险分层仍然具有挑战性.
- 现有的临床查工具对早期CKD识别有局限性.
研究的目的:
- 开发和验证一种新的,可解释的CKD风险预测模型.
- 改善早期检测和评估代谢综合征的老年患者中CKD的风险.
- 克服当前临床查方法的局限性.
主要方法:
- 利用了来自国家健康和营养检查调查和中国健康和退休长度研究的数据.
- 使用估计的淋巴膜过率 (eGFR) < 60 mL/min/1.73 m2 (CKD-EPI公式) 定义的CKD.
- 采用了10个机器学习算法,四个数据平衡策略,以及严格的特征选择,用于模型开发和验证.
主要成果:
- 最终的模型,结合尿酸与高密度脂蛋白胆固醇 (UHR) 的比率,年龄,尿酸和血液尿素 (BUN),通过后勤回归实现了0.864 (内部) 和0.831 (外部) 的预测准确度.
- 沙普利添加剂测量和受限立方线分析证实了关键预测因子与CKD风险之间的显著关联.
- 该模型展示了强大的性能和在验证队列中广泛的适用性.
结论:
- 通过使用常规临床指标,成功开发了一种可靠和可解释的CKD风险预测模型.
- 该模型为大规模的早期CKD查和初级保健机构的分层管理提供了有效的工具.
- 开发的模型具有很高的临床适用性,可以改善患者的治疗结果.
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