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: 10443874
• 负责人: Deborah E. Barnes
• 金额: $ 82.66万
• 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 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10443874
• 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; Persons; 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 implementation; 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，导致无法获得治疗 以及对患者及其家人的教育和支持，因此，NIA 已要求申请支持。 低成本工具的实用临床试验，以改善临床环境中认知能力下降的检测（RFA-AG- 20-051）。在 NIA 的试点资助下，我们使用机器学习开发了一种名为 eRADAR 的低成本工具。 （电子健康记录阿尔茨海默氏症和痴呆症评估规则），它使用易于访问的 电子健康记录 (EHR) 中的信息可帮助识别未确诊的痴呆症患者。 此外，我们采访了患者、护理人员、牧师和医疗保健系统领导者，以提供务实的信息 利益相关者强烈认为应该在临床环境中实施 eRADAR。 在现有临床关系的背景下通过初级保健实施，并且需要 我们当前的建议充分考虑了这一点。 在目标 1 中，我们将使用 EHR 数据来评估 eRADAR 在不同方面的性能。 两个医疗保健系统中的患者亚组，包括按种族/民族划分，以告知选择切 我们将选择一个最佳切入点用于临床环境中的目标痴呆症。 在目标 2 中，我们根据利益相关者的意见进行评估，平衡敏感性、特异性和阳性预测值。 将进行一项务实的临床试验，以确定是否将 eRADAR 实施作为 对高风险患者的支持外展流程将改善痴呆症的检测。 Kaiser Permanente Washington (KPWA) 内的初级保健诊所是一个综合医疗保健服务系统 华盛顿州和加州大学旧金山分校 (UCSF) 是一所城市学术医疗保健机构 该研究包括 6 个诊所，约有 24,000 名年龄≥65 岁的患者。 每个诊所、初级保健提供者 (PCP) 将被随机分配给高 eRADAR 患者 针对外展（干预）或常规护理（控制）的分数 - 我们的临床研究人员的角色。 旨在反映这些医疗保健系统中的现有角色，以最大限度地提高实用主义——将达到 对 eRADAR 评分高的患者进行认知障碍评估，进行随访 向 PCP 提出建议，并在诊断后为 eRADAR 评分较高的患者提供支持。 将跟踪治疗组以确定 eRADAR 对新诊断痴呆症的影响（主要 结果）根据 EHR 评估（再次，为了最大限度地提高实用性），我们将探讨其影响。 eRADAR 实施对次要结果的影响，包括医疗保健利用率和经验 如果这项实用试验成功，eRADAR 工具和流程可能会成为现实。 传播到其他医疗保健系统，有可能改善认知能力下降的检测、患者护理和 生活质量。