Building a Platform for Precision Anesthesia for the Geriatric Surgical Patient

项目总结 围手术期神经认知障碍(PNCD)影响约25%的患者 在手术后，在10%的老年外科患者中可以持续数月或数年。这个 手术后急性认知障碍的存在最终增加了患者的风险 发展中的痴呆症，如阿尔茨海默病。认知结果差会导致更长的时间 住院时间减少，生活质量下降，发病率和死亡率增加。不幸的是， 大约一半的老年人需要在他们余生的某个时间进行手术。 多年来，由于病因尚不清楚，目前还没有预防PNCD的干预措施。其中一个 在确定导致慢性认知损害的因素方面的主要挑战是缺乏 外科护理计划中的常规、全面的认知测试。这种缺陷在一定程度上是 由于缺乏移动、易用的认知测试平台。以确定围手术期 导致认知功能下降的生物标志物，我们将(1)综合常规、全面 手术前后的认知测试，以及(2)建立数据库和分析平台 挖掘这个多维数据集。总而言之，这旨在产生准确的模型来预先识别 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全面数据库 人口学和危险因素问卷调查结果，库中血液样本，术中 脑电(EEG)和包容性认知测试。这些数据将被收集 在整个患者互动过程中，从术前预约到一年后 手术过程和其他研究团队都可以使用。我们将把认知 在老年外科环境中测试和收集大规模数据，开创了新的先例 用于后续的多学科研究。这一结构将为我们提供机会 准确跟踪慢性疾病(如痴呆症)的认知下降情况。 在R33阶段，我们将开发一个能够挖掘这个多维数据的分析平台 数据集。这一阶段将包括脑电分析和使用质量细胞仪的深度免疫分析。 在这些分析的基础上，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病理的相互作用。我们的新式机器 学习工具使用先验知识来改进特征选择，从而解决了常见的 临床研究面临的挑战(在有限的患者中进行多项测量 人口)，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.