Low-cost detection of dementia using electronic health records data: validation and testing of the eRADAR algorithm in a pragmatic, patient-centered trial.

使用电子健康记录数据低成本检测痴呆症：在务实、以患者为中心的试验中验证和测试 eRADAR 算法。

• 批准号: 10266125
• 负责人: Deborah E. Barnes
• 金额: $ 90.33万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10266125
• 关键词: Academic Medical Centers; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; California; Caregivers; Clinic; Clinical; Clinical Research; Communities; Data; Data Element; Dementia; Detection; Development; Diabetes Mellitus; Diagnosis; Disease; Early Diagnosis; Education; Electronic Health Record; Emergency department visit; Equilibrium; Ethnic Origin; Family; Family member; Funding; Goals; Health Services Accessibility; Healthcare Systems; Impaired cognition; Inpatients; Integrated Delivery of Health Care; Intervention; Interview; Laboratories; Machine Learning; Measures; Mental Depression; Participant; Patient Care; Patient Education; Patients; Pattern; Performance; Population Heterogeneity; Pragmatic clinical trial; Predictive Value; Primary Health Care; Procedures; Process; Quality of Care; Quality of life; Race; Randomized; Randomized Controlled Trials; Recommendation; Request for Applications; Research Design; Research Support; Retrospective Studies; Risk; Role; San Francisco; Sensitivity and Specificity; Services; Stroke; Subgroup; Surveys; Symptoms; System; Testing; Traumatic Brain Injury; Underweight; Universities; Validation; Visit; Washington; Work; base; care providers; clinical care; comparison intervention; cost; dementia risk; design; electronic structure; experience; follow-up; formative assessment; health care service utilization; high risk; human old age (65+); improved; medication compliance; neuroimaging; outreach; patient oriented; patient population; patient subsets; pragmatic trial; preventive intervention; primary outcome; satisfaction; secondary outcome; sex; tool; treatment arm; treatment as usual; usual care arm

Nearly half of people currently living with dementia have not received a diagnosis, delaying access to treatment as well as education and support for the patient and family. Thus, NIA has requested applications to support pragmatic clinical trials of low-cost tools to improve detection of cognitive decline in clinical settings (RFA-AG- 20-051). With pilot funding from NIA, we used machine learning to develop a low-cost tool called eRADAR (electronic health record Risk of Alzheimer's and Dementia Assessment Rule), which uses easily accessible information in the electronic health record (EHR) to help identify patients with undiagnosed dementia. In addition, we interviewed patients, caregivers, clinicians, and healthcare system leaders to inform pragmatic implementation of eRADAR in clinical settings. Stakeholders felt strongly that such a tool should be implemented through primary care, in the context of existing clinical relationships, and would need to be accompanied by additional support for patients and clinicians. Our current proposal is heavily informed by this development work. In Aim 1, we will use EHR data to evaluate eRADAR's performance in different patient subgroups, including by race/ethnicity, in two healthcare systems to inform selection of cut- points for use in clinical settings. We will select an optimal cut-point to use for targeted dementia assessment with stakeholder input, balancing sensitivity, specificity, and positive predictive value. In Aim 2, we will perform a pragmatic clinical trial to determine whether implementing eRADAR as part of a supported outreach process to high-risk patients improves dementia detection. The setting will be primary care clinics within Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system in Washington State, and the University of California, San Francisco (UCSF), an urban, academic healthcare system with a diverse patient population. The study includes 6 clinics with ~24,000 patients age ≥65. Within each clinic, primary care providers (PCPs) will be randomly assigned to have their patients with high eRADAR scores targeted for outreach (intervention) or to usual care (control). Our clinical research staff—whose roles were designed to reflect existing roles within these healthcare systems to maximize pragmatism—will reach out to patients with high eRADAR scores, conduct an assessment for cognitive impairment, make follow-up recommendations to PCPs, and support patients after diagnosis. Patients with high eRADAR scores in both treatment arms will be followed to determine the impact of eRADAR on new diagnoses of dementia (primary outcome) as assessed from the EHR (again, to maximize pragmatism). In Aim 3, we will explore the impact of eRADAR implementation on secondary outcomes including healthcare utilization and experience of patients and family members. If this pragmatic trial is successful, the eRADAR tool and process could be spread to other healthcare systems, potentially improving detection of cognitive decline, patient care, and quality of life.

目前有近一半的痴呆症患者尚未得到诊断，从而延迟了治疗的机会 以及对患者和家人的教育和支持。这，NIA已要求申请支持 低成本工具的务实临床试验改善了临床环境认知能力下降的检测（RFA-AG- 20-051）。通过NIA的飞行员资助，我们使用机器学习开发了一种名为Eradar的低成本工具 （电子健康记录的阿尔茨海默氏症和痴呆评估规则的风险），该规则使用易于访问 电子健康记录（EHR）中的信息有助于鉴定痴呆症的患者。 此外，我们采访了患者，看护人，临床医生和医疗保健系统领导者，以告知务实 在临床环境中实施ERADAR。利益相关者强烈认为这种工具应该是 在现有的临床关系的背景下通过初级保健实施，需要 伴随着对患者和临床医生的额外支持。我们目前的提议已大大了解 发展工作。在AIM 1中，我们将使用EHR数据来评估Eradar在不同的表现 患者亚组，包括种族/种族，在两个医疗保健系统中，以告知切割的选择 - 用于临床环境的点。我们将选择一个最佳的切点，用于靶向痴呆 通过利益相关者输入，平衡灵敏度，特异性和积极的预测价值进行评估。在AIM 2中，我们 将执行务实的临床试验，以确定实施ERADAR是否作为 高风险患者的支持外展过程可改善痴呆症检测。设置将是 Kaiser Permanente Washington（KPWA）内的初级保健诊所，这是一个综合的医疗保健服务系统 在华盛顿州和加利福尼亚大学旧金山大学（UCSF），城市，学术医疗保健 具有潜水员人群的系统。该研究包括6个诊所，约24,000名患者≥65岁。之内 每个诊所，初级保健提供者（PCP）将被随机分配给其患者较高 针对外展（干预）或通常护理（控制）的分数。我们的临床研究人员 - 担任角色 旨在反映这些医疗保健系统中的现有角色，以最大程度地发挥实用主义 - 向超高分数的患者出去，进行认知障碍评估，进行随访 向PCP提出建议，并在诊断后支持患者。两者的埃达尔分数高的患者 将遵循治疗臂，以确定埃达尔对痴呆新诊断的影响（主要 结果）如EHR所评估（同样，以最大化实用主义）。在AIM 3中，我们将探索影响 在包括医疗保健利用和经验在内的二级成果实施的埃拉达尔实施 患者和家人。如果这项务实的试验成功了，则可以使用Eradar工具和过程 扩展到其他医疗保健系统，可能改善对认知能力下降，患者护理和 生活质量。