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.

抽象的 术前认知障碍在准备手术的老年人中很常见。尽管增长 术前认知/神经元完整性是围手术期和术后的危险因素的证据 手术不良结果，医疗保健系统不会系统地术前筛查认知。 临床研究人员尚未确定务实的术前认知筛查方法。我们的团队 成员已经开发了数字时钟绘图测试（DCDT），该工具可捕获微妙的行为 快速（5分钟）时钟绘图评估期间的变量。该工具提供的数据和好处 然而，在围手术期的环境中需要考虑。我们将在大量预先使用的情况下应用DCDT 手术患者（每年n = 5,000）以及新型机器学习算法，以解决三种特定的特定 目标。目标1：检查术前神经认知障碍的范围和分布 术前患者相对于非手术的老年人口统计学匹配的同龄人（可用n = 2,400 通过NIH/波士顿大学Framingham心脏研究）使用以前未观察到的DCDT图形运动 和决策变量； AIM 2：检查术前DCDT变量的预测有效性 术后，临床医生报告/医院记录的事件； AIM 3：检查术后6周，3-- DCDT和NIH Promis指标（n = 70），骨科（n = 70）的月份和1年变化 腹部 - 斜角患者（n = 70）和非手术同伴（n = 70）。对于观察性研究（AIM 1和2）， 个人>向UFHealth Prergical Prestrgical Clinic展示65年的个人将完成DCDT以及三个 作为标准临床评估的一部分，单词记忆测试和脆弱评估。手术和麻醉 详细信息将通过电子病历获取。临床上与临床相关的结果将包括 并发症，住院时间，护理成本，功能能力和死亡率。结果将得到补充 通过一个单独的纵向研究子组（AIM 3）在6周，3个月时完成NIH Promis指标， 和手术后1年。分析将重点放在分布DCDT特征的分布和簇上 在许多社会人口统计学，外科和麻醉因素中。 DCDT的预测价值将是 相对于临床结果建模。将比较DCDT和基线NIH Promis域的变化 术前和术后，并检查与纵向围手术期事件的相互作用。 Subaims：我们 将采用“深度学习”方法来识别术前患者的新特征 通过Framingham心脏研究获得的大量人口统计学的DCDT数据点。 符号汇总近似（SAX）的机器学习方法将表征相互作用 术前DCDT特征和术中麻醉敏感性。