开发基于电子健康记录的算法,以识别患有糖尿病视网膜病变的个体
Joseph H Breeyear1,2, Sabrina L Mitchell2,3, Cari L Nealon4
1Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 19, 2024
概括
开发的算法准确地识别了多个医疗保健系统的电子健康记录 (EHR) 的糖尿病视网膜病变 (DR) 病例和控制. 这些经过验证的方法比现有的DR检测自动化方法提供了更好的性能.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 生物医学数据科学 生物医学数据科学
背景情况:
- 糖尿病视网膜病变 (DR) 是导致视力丧失的主要原因,需要准确识别病例和对照进行研究.
- 电子健康记录 (EHR) 包含大量的患者数据,但需要强大的算法来精确的表型.
- 现有的自动化方法用于从EHR中识别DR可能缺乏足够的准确性.
研究的目的:
- 开发,验证和实施用于识别糖尿病视网膜病变 (DR) 病例和使用EHR数据进行控制的算法.
- 评估这些算法在各种电子健康记录系统中的性能.
- 为了实现大规模的DR队列的识别,用于遗传研究.
主要方法:
- 开发并验证了基于EHR的算法,使用ICD-9/10代码,健康因素和EPIC代码来识别DR病例.
- 定义了控制标准,包括糖尿病诊断和眼科检查.
- 在三个独立的EHR系统中测试了算法:VUMC,VANEOHS和MGB.
主要成果:
- 算法在医疗中心实现了高的正预测值 (PPV) 和负预测值 (NPV) (最低PPV为0.84,NPV为0.76).
- 业绩指标强,PPV从0.84到0.94不等,NPV从0.76到0.91.
- 与PheWAS算法相比,开发的方法显示了类似的PPV,但NPV要高得多.
- 在百万退伍军人计划中确定了超过62,000个DR病例,其中包括多元种族群体.
结论:
- 开发的算法展示了强大的和验证的性能,用于识别DR病例和控制从EHRs.
- 与现有的自动化方法相比,这些算法提供了更好的准确性,特别是在NPV中.
- 建议进行独立验证并整合特定于EHR的特征,以进一步提高算法性能.
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