使用电子健康记录和自我报告数据识别2型糖尿病的算法
Ben T Varghese1,2, Marlene E Girardo3, Ruchi Gupta4
1Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Metabolic syndrome and related disorders
|April 7, 2025
概括
结合电子健康记录和自我报告数据的算法准确地识别了2型糖尿病 (T2D). 这种方法改善了研究队列中的T2D分类,提高了研究数据的可靠性.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床研究 临床研究
背景情况:
- 准确识别患有2型糖尿病 (T2D) 的参与者对于临床研究至关重要.
- 仅依靠电子健康记录 (EHR) 或自我报告的数据在T2D分类准确性方面存在局限性.
研究的目的:
- 开发和验证一个算法,整合EHR和自我报告数据,以准确识别患有和没有T2D的个人.
- 为了提高T2D病例确定在大型生物库队列中的准确性.
主要方法:
- 利用了梅奥诊所生物库的数据,包括基线问卷和电子病历数据 (ICD代码,HbA1c,葡萄糖,药物).
- 开发了一种算法,将参与者分为T2D,没有T2D",只有自我报告的T2D"和"只有自我报告的没有T2D"类别.
- 使用手动图表审查作为黄金标准验证了算法的性能,计算正预测值 (PPV) 和负预测值 (NPV).
主要成果:
- 算法对57,000名参与者进行了分类:6,238人患有T2D,38,883人没有T2D,757人"只有自我报告的T2D",9759人"只有自我报告的没有T2D".
- 实现了高性能指标:PPV为96.0%,NPV为100%,整体准确率为99.5%.
- 在各分类组的年龄和性别分布中观察到显著的差异.
结论:
- 开发的算法证明了高准确性和可靠性,用于识别T2D和没有T2D的个人,使用组合的EHR和自我报告数据.
- 这种经过验证的算法为研究环境中的T2D确定提供了一个强大的工具,可能适用于与链接EHR数据的其他队列.
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