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.

项目摘要 围手术期神经认知障碍（PNCD）在此期间影响约25％的患者 手术后，在10％的老年手术患者中可以持续数月或数年。这 手术后急性认知障碍的存在会增加患者的风险 发展痴呆症，例如阿尔茨海默氏病。认知结果差会导致更长的时间 医院住院，生活质量降低，发病率和死亡率提高。很遗憾， 大约一半的老年人在剩下的时间需要进行手术程序 几年，并且由于病因尚不清楚，因此没有任何干预措施来预防PNCD。中的一个 确定导致慢性认知障碍的因素的主要挑战是缺乏 例行的，全面的认知测试在手术护理计划中。这个缺点是部分 由于缺乏移动，易于使用的认知测试平台。建立围手术期 生物标志物导致认知能力下降，我们将（1）整合常规，全面 认知测试前后手术，（2）建立一个数据库和一个分析平台 开采此多维数据集。这共同旨在产生准确的模型以预识别 有风险的患者并创建有针对性的治疗干预措施。 我们建议建立基础基础设施，以采用精确医学方法 老年手术患者。在R21阶段，我们将建立一个新颖的综合数据库 人口统计和风险因素问卷回答，库存的血液标本，术中 脑电图（EEG）和包容性认知测试。这些数据将被收集 在整个患者互动中，从术前约会到接下来的一年 手术程序，可供其他研究团队使用。我们将结合认知 在老年手术环境中测试和收集大规模数据，建立新的先例 对于随后的多学科研究。这种结构将使我们有机会 准确地跟踪认知能力下降，以降低慢性疾病，例如痴呆。 在R33阶段，我们将开发一个能够挖掘这种多维的分析平台 数据集。该阶段将使用质量细胞术包括脑电图分析和深度免疫分析。 在这些分析上分层，我们将构建创新的机器学习工具以识别功能 以及有助于急性和慢性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，从而解决一个常见 临床研究面临的挑战（有限患者的测量值很多 人口），因此将引起其他临床研究项目的广泛关注。