Building a Platform for Precision Anesthesia for the Geriatric Surgical Patient

为老年手术患者建立精准麻醉平台

• 批准号: 10697395
• 负责人: David Raymond Drover
• 金额: $ 77.71万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10697395
• 关键词: Acute; Address; Affect; Aging; Alzheimer' s Disease; Anesthesia procedures; Appointment; Attention; Attenuated; Biological; Biological Markers; Blinded; Blood Banks; Blood specimen; Brain; Caring; Characteristics; Chronic; Clinical; Clinical Data; Clinical Research; Cluster Analysis; Cognitive; Collaborations; Communities; Cytometry; Data; Data Analytics; Data Collection; Databases; Delirium; Dementia; Demographic Factors; Development; Dimensions; Disparate; Elasticity; Elderly; Electroencephalography; Electronic Health Record; Electrophysiology (science); Etiology; Foundations; Health; Immune; Immune response; Impaired cognition; Incidence; Individual; Inflammatory; Inflammatory Response; Infrastructure; Interdisciplinary Study; Intervention; Knowledge; Lead; Length of Stay; Longterm Follow-up; Machine Learning; Measurement; Measures; Memory; Mining; Modeling; Morbidity - disease rate; Neuropsychological Tests; Operative Surgical Procedures; Outcome; Pathology; Patients; Perioperative; Persons; Phase; Physiological; Postoperative Period; Precision Medicine Initiative; Predictive Factor; Process; Quality of life; Questionnaires; Research; Research Project Grants; Risk; Risk Assessment; Risk Factors; Risk Marker; Sentinel; Source; Speed; Standardization; Structure; Subgroup; Test Result; Testing; Therapeutic Intervention; Time; cognitive ability; cognitive testing; data analysis pipeline; executive function; feasibility testing; feature selection; follow-up; improved; innovation; interest; large scale data; mortality; multidimensional data; multidisciplinary; neurocognitive disorder; novel; novel strategies; participant enrollment; patient population; patient stratification; precision medicine; predictive modeling; prevent; research study; response; risk prediction; risk stratification; stem; targeted treatment; tool; verbal

PROJECT SUMMARY Perioperative neurocognitive disorders (PNCD) affect about 25% of patients in the period following surgery, and can persist for months or years in 10% of geriatric surgical patients. The presence of acute cognitive disturbances post-surgery increases the risk of patients eventually developing dementias such as Alzheimer's disease. Poor cognitive outcomes lead to longer hospital stays, decreased quality of life, and increased morbidity and mortality. Unfortunately, about half of elderly individuals require a surgical procedure at some time in their remaining years, and no interventions exist to prevent PNCD because the etiology is unclear. One of the main challenges in identifying the factors leading to chronic cognitive impairment is the lack of routine, comprehensive cognitive testing in the surgical care plan. This shortcoming is in part due to the lack of mobile, easy-to-use cognitive testing platforms. To establish the perioperative biomarkers contributing to cognitive decline, we will (1) integrate routine, comprehensive cognitive testing pre- and post-surgery, and (2) build a database and an analysis platform to mine this multidimensional dataset. Together, this aims to yield accurate models to pre-identify patients at risk and create targeted therapeutic interventions. We propose to build the foundational infrastructure for a precision medicine approach in geriatric surgical patients. In the R21 phase, we will build a novel comprehensive database of demographic and risk factor questionnaire responses, banked blood specimens, intraoperative electroencephalography (EEG), and inclusive cognitive testing. These data will be collected throughout the patient interaction, from the preoperative appointment through a year following the surgical procedure and available to other research teams. We will incorporate cognitive testing and collect large-scale data in the geriatric surgical setting, establishing a new precedent for subsequent multidisciplinary studies. This structure will afford us the opportunity to accurately track cognitive decline towards chronic conditions, such as dementia. In the R33 phase, we will develop an analysis platform capable of mining this multidimensional dataset. This phase will include EEG analysis and deep immune profiling using mass cytometry. Layered on these analyses we will build innovative machine learning tools to identify features and interactions contributing to both acute and chronic PNCD pathology. Our novel machine learning tools use prior knowledge to refine feature selection, thus addressing a common challenge faced by clinical research studies (having many measurements in a limited patient population), and will thus be of broad interest to other clinical research projects.

项目概要 围术期神经认知障碍 (PNCD) 影响该时期约 25% 的患者 手术后，10% 的老年手术患者可能会持续数月或数年。这 手术后出现急性认知障碍最终会增加患者的风险 发展为痴呆症，例如阿尔茨海默病。认知结果不佳会导致更长时间 住院时间、生活质量下降以及发病率和死亡率增加。很遗憾， 大约一半的老年人在其剩余时间的某个时候需要进行外科手术 多年来，由于病因尚不清楚，因此尚无预防 PNCD 的干预措施。中的一个 识别导致慢性认知障碍的因素的主要挑战是缺乏 手术护理计划中的常规、全面的认知测试。这个缺点部分是 由于缺乏移动、易于使用的认知测试平台。建立围手术期 导致认知能力下降的生物标志物，我们将 (1) 整合常规、综合 手术前和手术后的认知测试，以及（2）建立数据库和分析平台 挖掘这个多维数据集。总之，这旨在产生准确的模型来预先识别 处于危险中的患者并制定有针对性的治疗干预措施。 我们建议为精准医疗方法建立基础设施 老年外科患者。在R21阶段，我们将建立一个新颖的综合数据库 人口统计和危险因素问卷调查、储存的血液样本、术中 脑电图（EEG）和包容性认知测试。这些数据将被收集 从术前预约到术后一年的整个患者互动过程 手术程序可供其他研究团队使用。我们将把认知 在老年外科环境中进行测试并收集大规模数据，开创新先例 以供后续的多学科研究。这种结构将使我们有机会 准确跟踪慢性疾病（例如痴呆症）的认知能力下降。 在R33阶段，我们将开发一个能够挖掘这种多维度的分析平台。 数据集。此阶段将包括使用质谱流式分析仪进行脑电图分析和深度免疫分析。 基于这些分析，我们将构建创新的机器学习工具来识别特征 以及导致急性和慢性 PNCD 病理学的相互作用。我们的新颖机器 学习工具使用先验知识来完善特征选择，从而解决常见问题 临床研究面临的挑战（在有限的患者中进行许多测量 人群），因此将引起其他临床研究项目的广泛兴趣。