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: 10706563
• 负责人: Shenly Glenn
• 金额: $ 98.09万
• 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/10706563
• 关键词: Activities of Daily Living; Alzheimer' s disease related dementia; Brain; Brain Diseases; Breakthrough device; Caring; Classification; Clinic; Clinical Research; Cognitive; Communities; Complex; Data; Data Set; Dementia; Detection; Diagnosis; Diagnostic; Digital Signal Processing; Digital biomarker; Dimensions; Disease; Dropout; Economic Burden; Environment; Etiology; Health; Health Care Costs; Healthcare; Home; International; Intervention; Interview; Label; Measurement; Mental Depression; Metadata; Methods; Modeling; Participant; Patient Self-Report; Patient-Focused Outcomes; Patients; Performance; Population; Primary Care; Process Assessment; Prospective Studies; Quality of life; Research; Research Domain Criteria; Risk; Risk Reduction; Sample Size; Sampling; Self Administration; Self Assessment; Small Business Innovation Research Grant; Speed; Standardization; Telephone Interviews; Training; Work; amnestic mild cognitive impairment; brain health; cognitive interview; cognitive testing; comorbid depression; comorbidity; cost; data analysis pipeline; depressive symptoms; diagnostic platform; diagnostic tool; diagnostic value; genetic risk factor; geriatric depression; 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.

项目概要 拟议的计划旨在完成 Miro Health 完成 FDA 确定所需的工作 批准自动诊断遗忘性 MCI (aMCI)，这种疾病通常会导致阿尔茨海默病 及相关痴呆症 (ADRD)、非遗忘性 MCI (naMCI) 和晚年抑郁症 (LLD)。 工作涉及优化我们的数据处理管道和数字信号处理 基于收集的数据类型的方法和机器学习算法的改进 通过现实世界的环境而不是通过临床研究环境。 通过提供普遍可用的自助服务，改善患者治疗结果并降低医疗成本 管理移动大脑评估和诊断平台。 未来的研究参与者将从诊所和社区招募。 参与新颖的移动评估以及传统的认知和精神评估。 所得数据将用于：(1) 评估 Miro 的远程评估可用性；(2) （3）改进功能能力的量化，并在平台上添加RDoC元数据标签； 完善用于诊断 aMCI、naMCI、LLD 和共病 MCI+LLD 的 A.I. 模型。 因为晚年抑郁症常常与 MCI 相似，并且同时发生的抑郁症可能会加速 MCI 对痴呆的影响，抑郁症的进展识别和正确治疗可能 解决一些明显的 MCI 病例并减缓其他合并症的进展。 脑部疾病的发生很常见，并且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对最需要的患者的护理。 该计划涉及的工作将增强 Miro Health 的移动研究平台。 将 RDoC 元数据标签引入我们的移动研究平台的元数据模式 将有助于标准化临床研究，大幅减少每个项目所需的人员数量 研究并支持大规模可扩展性，从而产生精确、统一的数据集。 提高疾病病因研究的功效、可解释性和普遍性， 遗传风险因素和干预措施。