Building a Platform for Precision Anesthesia for the Geriatric Surgical Patient

项目摘要 围手术期神经认知障碍（PNCD）影响约25%的患者， 在10%的老年外科患者中，可持续数月或数年。的 术后急性认知障碍的存在增加了患者最终 发展成痴呆症如阿尔茨海默氏症。认知结果不佳导致更长的 住院时间、生活质量下降、发病率和死亡率增加。不幸的是， 大约一半的老年人在他们剩余的生命中的某个时候需要外科手术。 年，没有干预措施，以防止PNCD，因为病因尚不清楚。之一 确定导致慢性认知障碍的因素的主要挑战是缺乏 常规的，全面的认知测试在手术护理计划。这一缺陷在一定程度上 由于缺乏移动的、易于使用的认知测试平台。建立围手术期 生物标志物有助于认知下降，我们将（1）整合常规，全面 术前和术后的认知测试，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 患者处于危险之中，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， 老年外科病人。在R21阶段，我们将建立一个新的综合数据库， 人口统计学和风险因素问卷回答，库存血液标本，术中 脑电图（EEG）和包容性认知测试。这些数据将被收集 从术前预约到术后一年， 手术过程，并提供给其他研究小组。我们将结合认知 在老年外科环境中测试和收集大规模数据， 进行后续的多学科研究。这一结构将使我们有机会 准确跟踪认知能力下降对慢性疾病，如痴呆症。 在R33阶段，我们将开发一个分析平台，能够挖掘这种多维的 数据集。该阶段将包括EEG分析和使用质谱细胞术的深度免疫分析。 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 以及促进急性和慢性PNCD病理的相互作用。我们的新机器 学习工具使用先验知识来细化特征选择，从而解决了一个常见的 临床研究面临的挑战（在有限的患者中进行多次测量 人群），因此将对其他临床研究项目产生广泛的兴趣。

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.