Inflammatory and ADRD Biomarker Predictors of Perioperative Digital Clock Drawing

围手术期数字时钟绘图的炎症和 ADRD 生物标志物预测因子

• 批准号: 10121051
• 负责人: CATHERINE E PRICE
• 金额: $ 37.35万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10121051
• 关键词: Abdomen; Academic Medical Centers; Activities of Daily Living; Address; Adult; Alzheimer' s disease related dementia; American; American College of Surgeons; Anesthetics; Behavior; Behavioral; Boston; Cardiac; Characteristics; Chest; Clinic; Clinical; Cluster Analysis; Cognition; Cognitive; Complication; Comprehension; Computerized Medical Record; Coupled; Data; Data Science; Decision Making; Detection; Elderly; Event; Face; Framingham Heart Study; Frequencies; Funding; Healthcare Systems; Hospital Costs; Hospital Records; Hospitals; Impaired cognition; Individual; Inflammatory; Intervention; Length of Stay; Longitudinal Studies; Machine Learning; Measures; Medical; Medical center; Memory; Modeling; Modernization; Nerve Degeneration; Neurocognitive; Neurocognitive Deficit; Neurons; Observational Study; Operative Surgical Procedures; Orthopedics; Outcome; Participant; Patient Outcomes Assessments; Patients; Pelvis; Perioperative; Postoperative Complications; Postoperative Pain; Postoperative Period; Predictive Value; Price; Psyche structure; Recovery; Regression Analysis; Reporting; Research Personnel; Risk; Risk Factors; Sampling; Screening procedure; Secondary to; Series; Severities; Shapes; Societies; Subgroup; Suggestion; Testing; Time; United States National Institutes of Health; Universities; Validation; adverse outcome; age effect; base; care costs; clinically relevant; cognitive ability; cognitive change; cognitive process; cognitive testing; deep learning; deep neural network; digital; executive function; experience; flexibility; frailty; improved; innovation; insight; machine learning algorithm; member; mortality; multidisciplinary; novel; patient subsets; peer; performance tests; predictive marker; prospective; research clinical testing; screening; sociodemographics; spatiotemporal; tool

ABSTRACT Preoperative cognitive impairment is common among older adults preparing for surgery. Despite growing evidence that preoperative cognitive/neuronal integrity is a risk factor for perioperative insults and post- operative adverse outcomes, health care systems do not systematically pre-operatively screen for cognition. Clinical researchers have yet to identify a pragmatic approach to pre-operative cognitive screening. Our team members have developed the digital Clock Drawing Test (dCDT), a tool that captures subtle behavioral variables during a rapid (5-minute) clock drawing assessment. The data and benefit afforded by this tool have yet to be considered across perioperative contexts. We will apply the dCDT within a large number of pre- surgical patients (n=5,000 per year) coupled with novel machine learning algorithms to address three specific aims. Aim 1: examine range and distribution of preoperative neurocognitive impairment with older adult preoperative patients relative to non-surgical older adult demographically matched peers (available n=2,400 via NIH/Boston University Framingham Heart Study) using novel previously unobserved dCDT graphomotor and decision making variables; Aim 2: examine the predictive validity of presurgical dCDT variables on postoperative, clinician reported/hospital recorded events; Aim 3: examine pre to postoperative 6-week, 3- month, and 1-year change in dCDT and NIH PROMIS metrics for thoracic (n=70), orthopedic (n=70), major abdominal-pelvic patients (n=70), and non-surgery peers (n=70). For the observational studies (Aim 1 and 2), individuals > 65 years presenting to the UFHealth presurgical clinic will complete the dCDT as well as a three- word memory test and frailty assessment as part of the standard clinical evaluation. Surgical and anesthetic details will be acquired via the electronic medical record. Clinically-relevant outcomes will include complications, length of stay, cost of care, functional capacity, and mortality. Outcomes will be supplemented by a separate longitudinally-studied subgroup (Aim 3) completing NIH PROMIS metrics at 6 weeks, 3 months, and 1year after surgery. Analyses will focus on stratifying distributions and clusters of dCDT characteristics across numerous sociodemographic, surgical, and anesthetic factors. The predictive value of the dCDT will be modeled relative to clinical outcomes. Changes in dCDT and baseline NIH PROMIS domains will be compared pre- and post-operatively and examined for interactions with longitudinal perioperative events. Subaims: We will apply `deep learning' approaches to drawings to identify novel features of pre-surgical patients relative to a large sample of demographically equated dCDT data points available through the Framingham Heart Study. Symbolic aggregate approximation (SAX)-based machine learning approaches will characterize interactions between preoperative dCDT features and intraoperative anesthetic sensitivities.

摘要 术前认知障碍在准备手术的老年人中很常见。尽管不断增长 有证据表明，术前认知/神经元完整性是围手术期侮辱和术后侮辱的危险因素 手术不良后果，卫生保健系统不会系统地进行术前认知筛查。 临床研究人员尚未确定一种实用的术前认知筛查方法。我们队 成员们开发了数字钟表绘制测试(DCDT)，这是一个捕捉细微行为的工具 在快速(5分钟)的时钟绘制评估过程中的变量。此工具提供的数据和优势包括 尚未考虑到围手术期的情况。我们将在大量的预案中应用dCDT 外科患者(每年5,000人)，加上新的机器学习算法，以解决三个特定的 目标。目的1：检查老年人术前神经认知功能障碍的范围和分布 术前患者与非手术老年人在人口统计上匹配的同龄人(可用n=2,400 通过NIH/波士顿大学弗雷明翰心脏研究)使用以前未观察到的新dCDT图形运动器 和决策变量；目标2：检查术前dCDT变量对 术后，临床医生报告/医院记录的事件；目标3：术后6周、3周进行检查 胸部(n=70)、骨科(n=70)、重大(n=70)的dCDT和NIH prois指标的一个月和一年的变化 腹盆腔患者(n=70)和非手术患者(n=70)。对于观察性研究(目标1和2)， 65岁的个人在UFHealth术前诊所演讲将完成dCDT以及三个- 作为标准临床评估的一部分的单词记忆测试和脆性评估。外科手术和麻醉 详细信息将通过电子病历获得。与临床相关的结果包括 并发症、住院时间、护理费用、功能能力和死亡率。结果将得到补充 通过单独的纵向研究亚组(目标3)在6周、3个月、 术后1年。分析将重点放在dCDT特征的分层分布和聚类上 在众多的社会人口学、外科手术和麻醉因素中。DCDT的预测价值将是 根据临床结果进行建模。将比较dCDT和基线NIH Promis结构域的变化 手术前后，检查与纵向围手术期事件的相互作用。苏巴伊斯：我们 将把“深度学习”方法应用到图画上，以识别手术前患者相对于 通过弗雷明翰心脏研究获得的人口统计学上相等的dCDT数据点的大样本。 基于符号聚合近似(SAX)的机器学习方法将表征交互 术前dCDT表现与术中麻醉敏感性之间的关系。