对糖尿病并发症的访问到访问葡萄糖变化的预测能力
Xin Rou Teh1,2, Panu Looareesuwan3, Oraluck Pattanaprateep1
1Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Phaya Thai, Bangkok, 10400, Thailand.
BMC medical informatics and decision making
|March 18, 2025
概括
像HbA1c-CV和FPG-CV这样的葡萄糖变异性 (GV) 测量可以预测糖尿病并发症. 当HbA1c不可用时,禁食血葡萄糖 (FPG) 变化是一个可行的替代方案.
科学领域:
- 内分泌学 在内分泌学.
- 糖尿病学 糖尿病学
- 数据科学在医学中的数据科学
背景情况:
- 糖尿病并发症的预后因素对于患者管理至关重要.
- 葡萄糖变异性 (GVs) 的测量越来越被认可,但在预后模型中未得到充分利用.
- 这项研究调查了不同GVs对主要糖尿病并发症的预测能力.
研究的目的:
- 为了比较各种葡萄糖变性 (GV) 指标在预测心血管疾病 (CVD),糖尿病视网膜病变 (DR) 和慢性病 (CKD) 的有效性.
- 评估和比较传统统计模型与机器学习 (ML) 模型在预测这些并发症方面的表现.
- 为了确定哪些GV测量 (变化系数 (CV),标准偏差 (SD),时间变化) 是最具预测性.
主要方法:
- 对40662名2型糖尿病 (T2D) 患者 (2010-2019) 的回顾性队列研究.
- 从HbA1c和空腹血葡萄糖 (FPG) 获得的三种GV的分析:CV,SD和时间变化.
- 使用基线和纵向数据对考克斯比例危险回归,随机生存森林 (RSF) 和LTRC生存森林模型进行比较.
主要成果:
- 所有考虑的GVs (HbA1c-CV,HbA1c-SD,FPG-CV,FPG-SD) 都与心血管疾病,DR和CKD有关.
- 时间变化的GV与DR和CKD有关.
- 与传统模型相比,机器学习模型,特别是RSF,在DR和CKD预测方面表现稍好一些,传统方法和ML方法的整体表现相似. 对于DR预测而言,FPG GV的测量非常重要.
结论:
- 来自HbA1c和FPG的葡萄糖变异性 (GV) 测量显示了糖尿病并发症的可比预测性能.
- 禁食血葡萄糖 (FPG) 的变化可以作为一个实际的监测参数,特别是当HbA1c测量难以获得时.
- 传统和机器学习模型都为基于GV的糖尿病并发症预测提供了有价值的见解.
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