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使用基本的临床和实验室参数,开发和评估对疼痛性糖尿病外围神经病变的早期诊断方法
Yue Yang1, Lu Liu1, Hui-Hui Wang2
1Neuroscience Center, General Hospital of Ningxia Medical University, Yinchuan 750004, China.
Experimental gerontology
|July 30, 2025
概括
这项研究开发了一种预测模型,以识别患有疼痛性糖尿病外围神经病变 (PDPN) 风险的患者. 关键预测因素包括HbA1c,甘油三,BMI,疾病持续时间和尿液微专蛋白,有助于早期临床干预.
科学领域:
- 内分泌学 在内分泌学.
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 糖尿病外围神经病变 (DPN) 影响全球数百万人.
- 疼痛性糖尿病外围神经病变 (PDPN) 显著影响生活质量.
- 准确预测PDPN发作对于及时干预至关重要.
研究的目的:
- 开发和验证DPDN患者中PDPN的预测模型.
- 确定与PDPN发展相关的关键临床和实验室因素.
- 为临床医生提供一个工具,用于PDPN的早期风险分层.
主要方法:
- 从783名DPN患者的临床和实验室数据的回顾性分析.
- 使用疼痛神经病症4分数 (≥4) 识别PDPN.
- 通过LASSO回归选择变量,使用后勤回归构建模型,在独立队列上进行验证.
主要成果:
- 一个最佳预测模型包括血红蛋白A1c (HbA1c),甘油三 (TG),体重指数 (BMI),疾病持续时间和24小时尿液微蛋白 (UMA).
- 该模型表现出强大的歧视力,AUC为0.812 (建模) 和0.850 (验证).
- 在建模和验证组中都观察到出色的校准,表明可靠的预测.
结论:
- 开发的诺米图有效地预测了DPPN患者的PDPN发生情况.
- 这个工具可以帮助临床医生识别高风险个体.
- 早期发现有助于主动管理和改善患者的治疗结果.
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