Antecedents and Outcomes of Subjective Cognitive Decline: An Electronic Health Records and Artificial Intelligence Approach

主观认知下降的前因和结果：电子健康记录和人工智能方法

• 批准号: 10686969
• 负责人: Liqin Wang
• 金额: $ 10.84万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10686969
• 关键词: Acceleration; Adoption; Adult; Affect; Age; Agreement; Algorithms; Alzheimer' s disease related dementia; Appearance; Area; Artificial Intelligence; Award; Cardiovascular Diseases; Characteristics; Clinical; Clinical Informatics; Clinical Trials; Cognition; Cognitive; Cohort Studies; Data; Dementia; Development; Diagnosis; Early Diagnosis; Early Intervention; Elderly; Electronic Health Record; Enrollment; Ensure; Epidemiology; Exclusion; Exclusion Criteria; Factor Analysis; Glean; Goals; Health Personnel; Healthcare Systems; Impaired cognition; Impairment; Incidence; Individual; Informatics; Intervention; Investigation; Kidney Diseases; Knowledge; Machine Learning; Measures; Medical; Memory Loss; Mentorship; Methods; Natural Language Processing; Neurology; Outcome; Participant; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Phase; Population; Positioning Attribute; Prevalence; Procedures; Research; Research Personnel; Respiratory Tract Infections; Risk; Risk Factors; Sampling; Sleep Disorders; Statistical Methods; Surveys; Symptoms; Techniques; Technology; Testing; Training; biomedical informatics; case finding; clinical epidemiology; cognitive testing; cohort; dementia risk; experience; high risk; innovation; insight; longitudinal analysis; machine learning algorithm; machine learning model; machine learning prediction; medical attention; mild cognitive impairment; multimodality; neuropsychiatric disorder; novel therapeutics; observational cohort study; participant enrollment; patient stratification; performance tests; pre-clinical; process improvement; prognostic model; programs; risk prediction; risk prediction model; skill acquisition; skills; social; tool

PROJECT SUMMARY Early detection of Alzheimer’s disease and related dementias (ADRD) from electronic health records (EHRs) can facilitate participant enrollment in clinical trials and early intervention once clinically available. Subjective cognitive decline (SCD) can be an early manifestation of ADRD. Previous research in early detection of ADRD has focused on observational study cohorts, generally small in size and often with stringent medical exclusion criteria. Investigation of larger and more representative samples is needed to develop a full understanding of the underlying conditions, procedures, and/or interventions that can contribute to cognitive decline or accelerate progression to dementia in the population at large. The overall goal of this proposed research is to leverage large-scale EHR data and advanced informatics technology to develop case-finding methods for SCD and to advance the understanding of its risk factors and dementia outcomes in older adults. Preliminary data suggest that clinical notes and machine learning (ML) algorithms can be helpful to capture patients with early cognitive decline. However, identifying which patients with SCD are more likely to develop dementia is extremely challenging. During the K99 phase, the first aim will be to develop an informatics approach to identify a pre-dementia cohort (patients with evidence of a cognitive concern but no dementia). The second aim will identify the social and clinical characteristics of this cohort in the EHR, along with antecedent risk factors, and predictors for a dementia outcome. The two hypotheses are that 1) clinical conditions (eg, neuropsychiatric disorders, cardiovascular diseases, renal disease, respiratory infections, sleep disorders) and medications that deleteriously affect cognition will contribute to the initial appearance of cognitive decline; and 2) longitudinal, multimodal EHR data can be leveraged in ML models to stratify patients with high risk of dementia. To accomplish these goals, the applicant will leverage existing strengths in case identification, risk factor analyses, and prognostic modeling and gain additional knowledge and skills in three critical areas of training: (1) cognitive decline and ADRD, (2) clinical epidemiology, and (3) statistical methods. With the development of these skills, the applicant will be well positioned in the R00 phase to conduct the final aim: to study the antecedent risk factors and outcomes of SCD in a presumed SCD cohort (patients with both a subjective cognitive concern and normal performance on objective cognitive measures). Similar approaches to those used in the second aim will be employed to study the presumed SCD cohort. A highly innovative component of this project is the use of advanced artificial intelligence and large-scale EHR data for presumed SCD cohort identification, risk factor analyses, and early detection of dementia. The proposed study will provide some of the first insights into the characteristics and risk factors of SCD in the EHR, and predictors for dementia outcomes in SCD. For the applicant, this program will support a rapid transition to independence through a short period of intensive training and mentorship, which will seamlessly intertwine with the aims of the proposed project.

项目概要 通过电子健康记录 (EHR) 及早发现阿尔茨海默病和相关痴呆症 (ADRD) 可以促进参与者参加临床试验，并在临床可用后进行早期干预。主观 认知能力下降（SCD）可能是 ADRD 的早期表现。 ADRD 早期检测的既往研究 重点关注观察性研究队列，这些队列通常规模较小且通常具有严格的医疗排除 标准。需要对更大、更具代表性的样本进行调查，以充分了解 可能导致认知能力下降或的潜在条件、程序和/或干预措施 加速广大人群的痴呆症进展。这项研究的总体目标是 利用大规模 EHR 数据和先进的信息学技术开发 SCD 病例查找方法 并增进对其危险因素和老年人痴呆症结局的了解。初步数据 表明临床记录和机器学习 (ML) 算法有助于捕获早期患有 认知能力下降。然而，确定哪些 SCD 患者更有可能患上痴呆症是一项艰巨的任务 极具挑战性。在 K99 阶段，首要目标是开发一种信息学方法来识别 痴呆前队列（有认知问题证据但没有痴呆的患者）。第二个目标将 确定 EHR 中该队列的社会和临床特征以及先前的危险因素，以及 痴呆症结果的预测因子。这两个假设是 1) 临床状况（例如，神经精神疾病） 疾病、心血管疾病、肾脏疾病、呼吸道感染、睡眠障碍）和药物 有害地影响认知将导致认知能力下降的最初表现； 2）纵向， 多模式 EHR 数据可以在 ML 模型中利用，对痴呆症高风险患者进行分层。到 为了实现这些目标，申请人将利用案件识别、风险因素方面的现有优势 分析和预后建模，并获得三个关键培训领域的额外知识和技能： (1) 认知能力下降和 ADRD，(2) 临床流行病学，(3) 统计方法。随着发展 这些技能，申请人将在 R00 阶段处于有利地位，以实现最终目标：学习 假定的 SCD 队列中的先行危险因素和 SCD 结局（患者既具有主观 认知问题和客观认知测量的正常表现）。与这些类似的方法 第二个目标中使用的数据将用于研究假定的 SCD 队列。高度创新的组成部分 该项目将先进的人工智能和大规模 EHR 数据用于假定的 SCD 队列 痴呆症的识别、危险因素分析和早期发现。拟议的研究将提供一些 首次深入了解 EHR 中 SCD 的特征和风险因素以及痴呆症的预测因素 SCD 的结果。对于申请人来说，该计划将支持通过以下方式快速过渡到独立： 短期强化培训和指导，这将与该组织的目标无缝地交织在一起 拟议的项目。