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

项目总结 从电子健康记录(EHR)早期发现阿尔茨海默病和相关痴呆(ADRD) 可以促进参与者登记参加临床试验和一旦临床可用的早期干预。主观性 认知功能减退(SCD)可能是ADRD的早期表现。ADRD早期检测的研究进展 专注于观察性研究队列，通常规模较小，通常有严格的医疗排斥 标准。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潜在条件、程序和/或干预 在一般人群中加速发展为痴呆症。这项拟议研究的总体目标是 利用大规模的电子健康记录数据和先进的信息学技术，为SCD开发病例发现方法 并促进对其危险因素和老年人痴呆症结果的了解。初步数据 提示临床记录和机器学习(ML)算法可以帮助捕捉早期 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 极具挑战性。在K99阶段，第一个目标将是开发一种信息学方法来识别 痴呆症前期队列(有认知障碍但没有痴呆症的患者)。第二个目标是 确定EHR中这一队列的社会和临床特征，以及先前的风险因素，以及 痴呆症结局的预测因子。这两个假设是：1)临床条件(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 有害地影响认知将有助于认知衰退的最初出现；和2)纵向， 多模式EHR数据可以在ML模型中被利用来对痴呆症高危患者进行分层。至 实现这些目标，申请者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更多知识和技能： (1)认知衰退与ADRD，(2)临床流行病学，(3)统计学方法。随着……的发展 这些技能，申请者将处于有利地位的R00阶段进行的最终目标：学习 在推测的SCD队列中，SCD的先期危险因素和结果(既有主观上的 认知关注和在客观认知测量上的正常表现)。类似的方法 用于第二个目的将被用来研究推定的SCD队列。一个极具创新性的组件 该项目是使用先进的人工智能和大规模的EHR数据来推定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 对EHR中SCD的特征、危险因素和痴呆预测因素的初步认识 SCD的转归。对于申请者来说，该计划将支持通过 短期强化培训和指导，这将与 建议的项目。

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.