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

3D CAM：导出并验证 3 分钟的谵妄诊断评估

• 批准号: 7879401
• 负责人: EDWARD R MARCANTONIO
• 金额: $ 59.46万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7879401
• 关键词: Acute; Adopted; Adoption; Affect; Algorithms; Arts; Biometry; Cardiac Surgery procedures; Caring; Clinical; Clinical Management; Clinical assessments; Cognitive; Confusion; Data Set; Databases; Delirium; Detection; Development; Diagnosis; Diagnostic; Education; Elderly; Elements; Epidemiology; Evaluation; Factor Analysis; Failure; Funding; Future; Goals; Gold; Hospitals; Interview; Maps; Measurement; Methodology; Methods; Modeling; Nurses; Outcome; Patients; Performance; Persons; Physicians; Prevention; Protocols documentation; Research; Research Personnel; Screening procedure; Sensitivity and Specificity; Specificity; Structure; Syndrome; Testing; Translating; United States National Institutes of Health; Validation; abstracting; base; brief screening; clinical Diagnosis; clinical care; clinical practice; clinically relevant; cohort; cost; database structure; diagnosis standard; experience; improved; instrument; mental state; patient safety; prospective; public health relevance; response; simulation; statistics; theories; tool

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.

描述（申请人摘要）：谵妄（急性精神错乱）是一种病态且代价高昂的综合征，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，令人痛心的是，谵妄仍未得到充分认识。混乱评估法（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（简易精神状态检查加注意力测试）时间太长，无法在临床实践中广泛采用。开发更短的 MSE 来准确诊断谵妄的 CAM 将对临床实践和研究大有裨益。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 算法对谵妄进行 3 分钟诊断评估。我们提出 2 个开发和 2 个验证具体目标：1) 使用在急性后护理中获得的 4744 份谵妄评估数据集，我们将使用因子分析将 MSE 项目映射到关键 谵妄的认知领域和项目反应理论来识别最大化项目的子集 每个域的筛选效率。 2) 使用目标 1 中确定的项目和多变量模型 选择方法后，我们将开发3D-CAM。我们将使用模拟来完善 3D-CAM 心脏手术后进行的 752 次谵妄评估的独立数据集。 3）我们会 在 600 名老年人的新队列中前瞻性验证 3D-CAM 并测试其评估者间的可靠性 住院病人。我们将 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 老年临床医生经过详细评估后。 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 回应。我们提出的研究有很多优势，包括我们利用两大领域的能力 严格执行谵妄评估的数据库，使用最先进的测量 方法以及我们调查团队的专业知识。最重要的是，3D-CAM 将成为 识别谵妄的重要工具，从而改善住院患者的临床管理 长老们。 3D-CAM 还将促进新的质量改进举措，以提高患者安全 医院，以及教育和研究。公共卫生相关性：谵妄（急性精神错乱）影响 30-40% 的住院老年人，并导致不良的临床结果和更高的费用；然而，只有 20% 的病例得到治疗医生和护士的认可。我们研究的目标是推导、完善和验证 3D-CAM，这是一种针对谵妄的 3 分钟诊断评估。 3D-CAM 将提供简短、有效且可靠的评估，可以轻松集成到临床护理中， 从而促进准确诊断、适当评估和管理 这个常见、病态且代价高昂的问题。我们的研究有潜力改善 每年数百万住院老年人的临床护理质量和结果。