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

目前患有痴呆症的人中有近一半没有得到诊断，从而推迟了接受治疗的机会 以及对病人和家属的教育和支持。因此，NIA已请求应用程序支持 实用的低成本工具临床试验，以改进临床环境中认知能力下降的检测(RFA-AG- 20-051)。在NIA的试点资金支持下，我们使用机器学习开发了一个名为eRADAR的低成本工具 (电子健康记录阿尔茨海默氏症和痴呆症风险评估规则)，使用易于访问的 电子健康记录(EHR)中的信息，以帮助识别未诊断的痴呆症患者。在……里面 此外，我们采访了患者、护理人员、临床医生和医疗保健系统领导人，以告知务实 ERADAR在临床环境中的实施。利益相关者强烈认为，这样的工具应该是 在现有临床关系的背景下，通过初级保健实施，并需要 伴随着对患者和临床医生的额外支持。我们目前的建议在很大程度上受到了这个因素的影响 开发工作。在目标1中，我们将使用EHR数据来评估eRADAR在不同环境下的性能 两个医疗保健系统中的患者亚组，包括按种族/民族分组，以通知选择Cut- 在临床环境中使用的要点。我们将选择一个用于靶向痴呆的最佳切入点 利用利益相关者的意见进行评估，平衡灵敏度、特异度和积极预测值。在目标2中，我们 将进行一项务实的临床试验，以确定将eRADAR作为 对高危患者的支持外展流程改善了痴呆症的检测。该设置将为 Kaiser Permanente Washington(KPWA)内的初级保健诊所，一个集成的医疗保健提供系统 在华盛顿州，和加州大学旧金山分校(UCSF)，一家城市，学术医疗 具有多样化患者群体的系统。这项研究包括6家诊所，约24,000名年龄在≥65岁的患者。在 每个诊所、初级保健提供者(PCP)将被随机分配为其患者具有高eRADAR 针对外展(干预)或常规护理(对照)的分数。我们的临床研究人员-他们的角色 旨在反映这些医疗保健系统中的现有角色，以最大限度地实现实用主义-将达到 对eRADAR评分较高的患者，进行认知障碍评估，进行随访 向初级保健医生提供建议，并在确诊后为患者提供支持。两种情况下eRADAR得分均高的患者 将对治疗武器进行跟踪，以确定eRADAR对新诊断的痴呆症的影响(初级 从《电子健康记录》评估的结果)(同样，为了最大限度地实现实用主义)。在目标3中，我们将探讨其影响 实施eRADAR对次要结果的影响，包括医疗保健利用率和 病人和家属。如果这一务实的试验成功，eRADAR工具和过程可能是 传播到其他医疗系统，潜在地改善对认知功能下降的检测、患者护理和 生活质量。

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.