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.

项目摘要 从电子健康记录（EHR）中早期发现阿尔茨海默病和相关痴呆（ADRD） 可以促进参与者在临床试验中的登记和一旦临床可用的早期干预。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 主要集中在观察性研究队列，通常规模较小，通常有严格的医疗排除 的搜索.需要对更大和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的基础疾病、程序和/或干预措施，或 会加速老年痴呆症的发展这项拟议研究的总体目标是 利用大规模EHR数据和先进的信息技术，开发SCD病例发现方法 并进一步了解其风险因素和老年人痴呆症的后果。初步数据 这表明，临床笔记和机器学习（ML）算法可以帮助捕获早期患者， 认知能力下降然而，确定哪些SCD患者更有可能发展为痴呆症， 极具挑战性。在K99阶段，第一个目标是开发一种信息学方法， 痴呆前队列（有认知问题证据但无痴呆的患者）。第二个目标将 在EHR中识别该队列的社会和临床特征，沿着先前的风险因素，以及 痴呆症预后的预测因子这两个假设是：1）临床条件（例如，神经精神 疾病、心血管疾病、肾脏疾病、呼吸道感染、睡眠障碍）和 严重影响认知将有助于认知下降的最初出现;和2）纵向， 多模态EHR数据可以在ML模型中被利用来对具有高痴呆风险的患者进行分层。到 为了实现这些目标，申请人将利用现有的优势，在案件识别，风险因素 分析和预测建模，并在三个关键培训领域获得额外的知识和技能： (1)认知功能减退和ADRD，（2）临床流行病学，（3）统计学方法。的发展 这些技能，申请人将很好地定位在R 00阶段进行的最终目标：研究 在假定的SCD队列中，SCD的前因风险因素和结局（同时具有主观 认知关注和客观认知测量的正常表现）。类似的方法， 将采用第二个目标中使用的方法研究假定的SCD队列。一个高度创新的组成部分， 该项目是使用先进的人工智能和大规模的EHR数据为假定的SCD队列 识别，风险因素分析和早期发现痴呆症。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，以及痴呆的预测因素 SCD的结果。对于申请人来说，该计划将通过以下方式支持快速过渡到独立 短期的强化培训和指导，这将无缝地与目标的 拟议项目。