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; 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 电子健康记录（EHRS）的阿尔茨海默氏病和相关痴呆症（ADRD）的早期检测 一旦临床可用，就可以促进参与者参加临床试验和早期干预。主观 认知能力下降（SCD）可能是ADRD的早期表现。早期发现ADRD的研究 专注于观察研究队列，通常很小，通常具有严格的医学排除 标准。需要对更大和更具代表性的样本进行调查，以便对 可能导致认知能力下降或 总体人口加速对痴呆症的进展。这项拟议研究的总体目标是 利用大规模的EHR数据和高级信息技术来开发SCD的案例调查方法 并促进对老年人的危险因素和痴呆症结果的理解。初步数据 建议临床笔记和机器学习（ML）算法有助于捕获早期患者 认知能力下降。但是，确定哪些SCD患者更可能患痴呆症 极其挑战。在K99阶段，第一个目的是开发一种信息，以识别 痴呆前队列（具有认知问题的证据但没有痴呆症的患者）。第二个目标 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 预测痴呆症结果。这两个假设是1）临床状况（例如，神经精神病学 疾病，心血管疾病，肾脏疾病，呼吸道感染，睡眠障碍）和药物 细心影响认知将有助于认知能力下降的初始出现； 2）纵向， 可以在ML模型中利用多模式EHR数据来分层具有高痴呆症风险的患者。到 实现这些目标，申请人将在识别危险因素的情况下利用现有的优势 分析，预后建模并在培训的三个关键领域中获得其他知识和技能： （1）认知能力下降和ADRD，（2）临床流行病学和（3）统计方法。随着 这些技能，申请人将在R00阶段良好地定位，以实现最终目的：研究 在介绍的SCD队列中，SCD的先决危险因素和结果（均为主观的患者 在客观认知测量中的认知问题和正常表现）。那些类似的方法 将在第二个目标中使用用于研究提出的SCD队列。高度创新的组成部分 该项目是使用高级人工智能和大规模EHR数据用于推测的SCD队列 鉴定，危险因素分析和痴呆症的早期检测。拟议的研究将提供一些 对EHR中SCD的特征和风险因素的首次见解和痴呆的预测因子 SCD的结果。对于申请人，该计划将支持通过一个快速过渡到独立性 密集培训和心态的短期，这将无缝地与 拟议项目。