The Proactive Screening and diagnosis of mild cognitive impairment and depression in patients aged 65 and over: An Implementation Study

65 岁及以上患者轻度认知障碍和抑郁症的主动筛查和诊断：实施研究

• 批准号: 10551816
• 负责人: Shenly Glenn
• 金额: $ 149.14万
• 依托单位: MIRO HEALTH INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10551816
• 关键词: Activities of Daily Living; Alzheimer' s disease related dementia; Biological Markers; Brain; Brain Diseases; Caring; Classification; Clinic; Clinical Research; Cognitive; Communities; Complex; Data; Data Set; Dementia; Detection; Devices; Diagnosis; Diagnostic; Digital Signal Processing; Dimensions; Disease; Dropout; Economic Burden; Environment; Etiology; Health; Health Care Costs; Healthcare; Home; International; Intervention; Interview; Label; Lead; Measurement; Mental Depression; Metadata; Methods; Modeling; Participant; Patient Self-Report; Patient-Focused Outcomes; Patients; Performance; Population; Primary Health Care; Process Assessment; Prospective Studies; Quality of life; Research; Research Domain Criteria; Risk; Sample Size; Sampling; Self Administration; Small Business Innovation Research Grant; Speed; Standardization; Telephone Interviews; Training; Work; amnestic mild cognitive impairment; base; cognitive interview; cognitive testing; comorbid depression; comorbidity; cost; data analysis pipeline; depressive symptoms; diagnostic platform; diagnostic tool; diagnostic value; digital; genetic risk factor; geriatric depression; health economics; human old age (65+); implementation study; improved; mHealth; machine learning algorithm; machine learning model; meetings; mild cognitive impairment; neuropsychiatry; novel; programs; recruit; remote assessment; screening; smartphone based assessment; tool; usability

Project Summary The proposed program aims to complete the work needed for Miro Health to finalize FDA approval for the automated diagnosis of amnestic MCI (aMCI) which often leads to Alzheimer’s and related dementias (ADRD), non-amnestic MCI (naMCI), and late life depression (LLD). The work involves the optimization of our data processing pipeline and digital signal processing methods and the refinement of our machine learning algorithms based on data types collected via real-world settings rather than through clinical research environments. Our objective is to improve patient outcomes and reduce healthcare costs by providing a universally available, self- administered mobile brain assessment and diagnostic platform. Prospective study participants will be recruited from clinics and the community. Participants will participate in novel mobile assessments and traditional cognitive and psychiatric assessments. The resulting data will be used to: (1) Assess usability of Miro for remote assessment; (2) Improve quantification of functional abilities and add RDoC metadata labels to Platform; (3) Refine A.I. models for the diagnosis of aMCI, naMCI, LLD, and comorbid MCI+LLD. Because late-life depression often mimics MCI and co-occurring depression may hasten the progression of MCI toward dementia, the identification and proper treatment of depression may resolve some apparent cases of MCI and slow the progression of others. Comorbid presentation of brain disorders is common and the increased availability of tools that can accurately and reliably identify complex comorbidities will improve care for our neediest patients. The work involved in this program will enhance Miro Health’s Mobile Research Platform. The introduction of RDoC metadata labels into our Mobile Research Platform’s metadata schema will help standardize clinical research, dramatically reduce the number of staff needed per study, and support massive scalability. Enhancing tools that yield precise, uniform data sets will improve the power, interpretability, and generalizability of studies examining disease etiology, genetic risk factors, and interventions.

项目摘要 拟议的计划旨在完成Miro Health完成FDA所需的工作 批准自动诊断遗忘型MCI（aMCI），这通常会导致阿尔茨海默氏症 和相关痴呆（ADRD）、非遗忘型MCI（naMCI）和晚年抑郁症（LLD）。的 工作涉及我们的数据处理管道和数字信号处理的优化 方法和我们的机器学习算法的基础上收集的数据类型的改进 通过现实世界的设置，而不是通过临床研究环境。我们的目标是 通过提供普遍可用的自助服务，改善患者治疗效果并降低医疗保健成本 管理的移动的大脑评估和诊断平台。 将从诊所和社区招募前瞻性研究参与者。参与者将 参与新型移动的评估和传统的认知和精神评估。 所得数据将用于：（1）评估Miro用于远程评估的可用性;（2） 完善功能能力量化，平台增加RDoC元数据标签;（3） 优化人工智能aMCI、naMCI、LLD和共病MCI+LLD的诊断模型。 因为晚年抑郁症通常与MCI相似，同时发生的抑郁症可能会加速MCI的发生。 MCI向痴呆的发展，抑郁症的识别和适当的治疗可能 解决一些明显的MCI病例，减缓其他病例的进展。共病先露 大脑疾病是常见的，越来越多的工具可以准确地， 可靠地识别复杂的合并症将改善我们最需要的患者的护理。 该计划所涉及的工作将增强米罗健康的移动的研究平台。的 将RDoC元数据标签引入到我们的移动的研究平台的元数据模式中 这将有助于标准化临床研究，大大减少每个项目所需的工作人员数量， 学习，并支持大规模的可扩展性。加强产生精确、统一数据集的工具， 提高疾病病因学研究的效力、可解释性和可推广性， 遗传风险因素和干预措施。