Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering. Indiana University, Bloomington, Indiana, USA.
Department of Epidemiology, School of Public Health. Indiana University, Indianapolis, Indiana, USA.
J Am Geriatr Soc. 2023 Sep;71(9):2966-2974. doi: 10.1111/jgs.18426. Epub 2023 May 30.
The timely detection of Alzheimer's disease and other related dementias (ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer opportunities for enhancing the timely detection of ADRD by facilitating the development of passive digital markers (PDMs). We conducted a systematic evidence review to identify studies that describe the development, performance, and validity of EHR-based PDMs for ADRD.
We searched the literature published from January 2000 to August 2022 and reviewed cross-sectional, retrospective, or prospective observational studies with a patient population of 18 years or older, published in English that collected and interpreted original data, included EHR as a source of digital data, and had the primary purpose of supporting ADRD care. We extracted relevant data from the included studies with guidance from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and used the US Preventive Services Task Force criteria to appraise each study.
We included and appraised 19 studies. Four studies were considered to have a fair quality, and none was considered to have a good quality. The functionality of the PDMs varied from detecting mild cognitive impairment, Alzheimer's disease or ADRD, to forecasting stages of ADRD. Only seven studies used a valid reference diagnostic method. Nine PDMs used only structured EHR data, and five studies provided complete information on the race and ethnicity of its population. The number of features included in the PDMs ranges from 10 to 853, and the PMDs used a variety of statistical and machine learning algorithms with various time-at-risk windows. The area under the curve (AUC) for the PDMs varied from 0.67 to 0.97.
Although we noted heterogeneity in the PDMs development and performance, there is evidence that these PDMs have the potential to detect ADRD at earlier stages.
及时发现阿尔茨海默病和其他相关痴呆症（ADRD）的效果并不理想。电子健康记录（EHR）中已经存储的数字数据为通过促进被动数字标志物（PDM）的开发来增强 ADRD 的及时检测提供了机会。我们进行了系统的证据回顾，以确定描述用于 ADRD 的基于 EHR 的 PDM 的开发、性能和有效性的研究。
我们搜索了 2000 年 1 月至 2022 年 8 月期间发表的文献，并审查了横断面、回顾性或前瞻性观察性研究，这些研究的患者人群为 18 岁或以上，以英文发表，收集和解释原始数据，将 EHR 作为数字数据来源，并将支持 ADRD 护理作为主要目的。我们根据预测模型研究的批判性评估和数据提取清单提取了纳入研究的相关数据，并根据美国预防服务工作组的标准对每项研究进行了评估。
我们纳入并评估了 19 项研究。其中四项研究被认为具有中等质量，没有一项研究被认为具有高质量。PDM 的功能从检测轻度认知障碍、阿尔茨海默病或 ADRD 到预测 ADRD 的阶段不等。只有七项研究使用了有效的参考诊断方法。九项 PDM 仅使用结构化 EHR 数据，五项研究提供了其人群种族和民族的完整信息。PDM 中包含的特征数量从 10 到 853 不等，PDM 使用了各种统计和机器学习算法以及各种风险时间窗口。PDM 的曲线下面积（AUC）从 0.67 到 0.97 不等。
尽管我们注意到 PDM 的开发和性能存在异质性，但有证据表明这些 PDM 有可能更早地发现 ADRD。
