PRECEDE: PREsurgical Cognitive Evaluation via Digital clockfacEdrawing

PRECEDE：通过数字时钟进行术前认知评估

• 批准号: 9975669
• 负责人: CATHERINE E PRICE
• 金额: $ 42.08万
• 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/9975669
• 关键词: Abdomen; Academic Medical Centers; Activities of Daily Living; Address; Adult; 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; Evaluation; Event; Face; Framingham Heart Study; Frequencies; Funding; Healthcare Systems; Hospital Costs; Hospital Records; Hospitals; Impaired cognition; Individual; 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; 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。尽管成长 有证据表明术前认知/神经元完整性是围手术期损伤和术后损伤的危险因素 由于手术不良后果，医疗保健系统没有系统地进行术前认知筛查。 临床研究人员尚未找到一种实用的术前认知筛查方法。我们的团队 成员开发了数字时钟绘图测试（dCDT），这是一种捕捉微妙行为的工具 快速（5 分钟）钟表绘图评估期间的变量。该工具提供的数据和好处 仍有待在围手术期环境中考虑。我们将在大量的预演中应用 dCDT 手术患者（每年 5,000 名）结合新颖的机器学习算法来解决三个特定问题 目标。目标 1：检查老年人术前神经认知障碍的范围和分布 术前患者相对于人口统计匹配的非手术老年人的同龄人（可用 n=2,400 通过 NIH/波士顿大学弗雷明汉心脏研究）使用新颖的先前未观察到的 dCDT 书写电机 和决策变量；目标 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；目标 3：检查术前至术后 6 周、3- 胸科 (n=70)、骨科 (n=70)、重大科的 dCDT 和 NIH PROMIS 指标的月度和一年变化 腹部盆腔患者 (n=70) 和未接受手术的患者 (n=70)。对于观察性研究（目标 1 和 2）， 到 UFHealth 术前诊所就诊的年龄 > 65 岁的个人将完成 dCDT 以及三项 单词记忆测试和虚弱评估作为标准临床评估的一部分。手术和麻醉 详细信息将通过电子病历获取。临床相关结果将包括 并发症、住院时间、护理费用、功能能力和死亡率。成果将予以补充 由一个单独的纵向研究小组（目标 3）在 6 周、3 个月时完成 NIH PROMIS 指标， 以及手术后 1 年。分析将重点关注 dCDT 特征的分层分布和聚类 跨越许多社会人口、手术和麻醉因素。 dCDT 的预测值为 与临床结果相关的模型。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。子目标：我们 将应用“深度学习”方法来绘图，以识别术前患者相对于患者的新特征 通过 Framingham 心脏研究可获得人口统计学上等同的 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 特征与术中麻醉敏感性之间的关系。