Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review

Abstract

BackgroundThe 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.MethodsWe 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.ResultsWe 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.ConclusionAlthough we noted heterogeneity in the PDMs development and performance, there is evidence that these PDMs have the potential to detect ADRD at earlier stages.

Dementia Ideal Care: Ecosystem Map of Best Practices and Care Pathways Enhanced by Technology and Community

“…These risk stratification and early detection efforts can be enhanced with technologies such as datamining electronic health records (EHRs) [73,78,79]; analyzing data on social determinants of health (SDoH); and annually comparing scores from brain game apps, digital cognitive assessments, and emerging devices for remote monitoring of digital biomarkers [15,55].…”

“…To leverage the potential of digital monitoring and/or AI/ML analysis of PLWDs' data for patterns, predictions, and care recommendations-data from disparate sensors, apps, EHRs, and health systems need to be integrated through common data standards and data exchange protocols [15,31,55]. Data collection and data aggre-gation also needs to be done in an ethical, secure, and nonburdensome manner, not only for research or registry purposes [62,74], but also for daily service operations [35], care coordination, quality improvement [58,67,113] (see below), and analytics for population health [6,15,51,55,78,100].…”