3D CAM: Deriving and Validating a 3 minute Diagnostic Assessment for Delirium

描述（申请人摘要）：谵妄（急性意识模糊）是一种病态和昂贵的综合征，影响30-40%的住院老年人。在NIH的支持下，我们在定义谵妄的流行病学和制定预防和治疗策略方面取得了实质性进展。然而，在大多数临床环境中，谵妄仍然令人痛心地认识不足。混淆评估方法（CAM）算法已成为诊断谵妄的“金标准”。然而，CAM在完成之前需要进行精神状态检查（MSE），而推荐的MSE，即简易精神状态检查加注意力测试，对于临床实践中的广泛采用来说太长了。开发一种更短的MSE，允许准确的CAM诊断谵妄将是非常有益的临床实践和研究。利用PI最近完成的NIH资助研究中的两个大型谵妄评估数据库，我们建议开发，完善和验证3D-CAM：使用CAM算法进行谵妄的3分钟诊断评估。我们提出了2个开发和2个验证的具体目标：1）使用在急性期后护理中获得的4744个谵妄评估的数据集，我们将使用因子分析将MSE项映射到关键 认知领域的谵妄，和项目反应理论，以确定一个子集的项目，最大限度地提高了 每个域的筛选效率。2)使用目标1和多变量模型中确定的项目 选择方法，我们将开发3D-CAM。我们将使用模拟来改进3D-CAM， 心脏手术后进行的752次谵妄评估的独立数据集。3)我们将 前瞻性验证3D-CAM并在600名老年人的新队列中测试其评分者间可靠性 住院病人。我们将把3D-CAM的性能与两个黄金标准进行比较： 完整的CAM评估和基于DSM-IV的谵妄临床诊断，由经验丰富的 经过详细评估后，老年临床医生。4)我们将比较3D-CAM的性能， CAM-ICU，另一种针对CAM定义的谵妄的简短筛查方案，不使用言语 应答我们提出的研究有许多优势，包括我们利用2大 严格执行谵妄评估的数据库，使用最先进的测量方法 方法论和我们调查团队的专业知识最重要的是，3D-CAM将是一个 识别谵妄的重要工具，从而改善住院患者的临床管理 长老3D-CAM还将促进新的质量改进计划，以提高患者的安全性， 医院，以及教育和研究。公共卫生相关性：谵妄（急性意识模糊）影响30-40%的住院老年人，并导致临床结果不佳和成本较高;然而，只有20%的病例被治疗医生和护士识别。我们研究的目标是推导、完善和验证3D-CAM，这是一种3分钟的谵妄诊断评估。3D-CAM将提供一个简短、有效和可靠的评估，可以很容易地整合到临床护理中， 从而有助于准确的诊断，以及适当的评估和管理 这个常见的病态的昂贵的问题我们的研究有可能改善 临床护理质量和每年数百万住院老年人的结果。

DESCRIPTION (applicant's abstract): Delirium (acute confusion) is a morbid and costly syndrome that affects 30-40% of hospitalized elders. With NIH support, we have made substantial progress defining the epidemiology of delirium and developing strategies for its prevention and treatment. However, in most clinical settings, delirium remains distressingly under-recognized. The Confusion Assessment Method (CAM) algorithm has become the "gold standard" for diagnosis of delirium. However, the CAM requires a mental status examination (MSE) prior to its completion, and the recommended MSE, the Mini-Mental State Examination plus attentional testing, is too long for widespread adoption in clinical practice. Development of a shorter MSE that allows accurate CAM diagnosis of delirium would be of great benefit for clinical practice and research. Leveraging two large databases of delirium assessments from the PI's recently completed NIH-funded studies, we propose to develop, refine, and validate the 3D-CAM: a 3-minute diagnostic assessment for delirium using the CAM algorithm. We propose 2 development and 2 validation Specific Aims: 1) Using a dataset of 4744 delirium assessments obtained in post-acute care, we will use factor analysis to map MSE items to key cognitive domains of delirium, and item response theory to identify a subset of items that maximize the screening efficiency for each domain. 2) Using the items identified in Aim 1 and multivariable model selection methods, we will develop the 3D-CAM. We will refine the 3D-CAM using simulations in an independent dataset of 752 delirium assessments conducted after cardiac surgery. 3) We will prospectively validate the 3D-CAM and test its inter-rater reliability in a new cohort of 600 elderly hospitalized patients. We will compare the performance of the 3D-CAM with two gold standards: the full CAM assessment and a DSM-IV-based clinical diagnosis of delirium made by an experienced geriatric clinician after a detailed assessment. 4) We will compare the performance of the 3D-CAM with the CAM-ICU, another brief screening protocol for CAM-defined delirium that does not use verbal responses. Our proposed research has numerous strengths, including our ability to leverage 2 large databases of rigorously performed delirium assessments, use of state-of-the-art measurement methodology, and the expertise of our investigative team. Most importantly, the 3D-CAM will be a critical tool for recognition of delirium, thereby improving its clinical management among hospitalized elders. The 3D-CAM will also facilitate new quality improvement initiatives to improve patient safety in hospitals, as well as education and research. PUBLIC HEALTH RELEVANCE: Delirium (acute confusion) affects 30-40% of hospitalized elders, and leads to poor clinical outcomes and higher costs; yet, only 20% of cases are recognized by the treating physicians and nurses. The goal of our research is to derive, refine and validate the 3D-CAM, a 3 minute diagnostic assessment for delirium. The 3D-CAM will provide a short, valid, and reliable assessment that can be readily integrated into clinical care, thereby facilitating accurate diagnosis, and appropriate evaluation and management of this common, morbid, and costly problem. Our research has the potential to improve the quality of clinical care and outcomes of millions of elders who are hospitalized each year.