在血液透析中对性别特定的肉类症查:从下肢强度和生理指标的洞察力
Yujie Yang1,2, Hualong Liao3, Yang Chen1,2
1Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
BMC nephrology
|May 19, 2025
概括
机器学习模型在使用下肢肌肉强度的维护血液透析患者中有效选肉类. 性别特定的模型对早期检测和治疗这种疾病充满希望.
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
- 生物医学工程 生物医学工程
背景情况:
- 在维护血液透析 (MHD) 患者中,肉类是普遍存在的,增加了跌倒和死亡风险.
- 在这个人群中,下肢肌肉强度受到萨科佩尼亚的显著影响.
研究的目的:
- 开发和验证基于机器学习的辅助查模型,用于MHD患者的肉症.
- 为了研究下肢肌肉强度指标对 Sarcopenia 查的有用性.
- 为了探索萨科佩尼亚查模型中的性别特异性差异.
主要方法:
- 在中国对164名MHD患者进行的横截面研究.
- 使用2019年亚洲麻症工作组标准进行麻症评估.
- 使用下肢肌肉强度和其他生理指标开发特定性别的二元分类模型.
- 应用分层混和合成少数超标采样技术.
主要成果:
- 性别特定的模型显示出良好的区分能力:男性的AUC为79%,女性为80%.
- 将下肢肌肉强度与其他指标相结合,提高了女性模型的查性能,达到90%的AUC.
结论:
- 利用下肢肌肉力量的机器学习模型对于MHD患者的辅助 Sarcopenia查非常有价值.
- 性别特定的模型为这个脆弱群体的早期发现和管理提供了有效的工具.
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