Harvard Medical School, Beth Israel Deaconess Medical Center, Division of Gerontology, Institute for Aging Research, Hebrew SeniorLife, 1200 Centre Street, Boston, MA 02131, USA.
BMC Med Res Methodol. 2013 Jan 22;13:8. doi: 10.1186/1471-2288-13-8.
Delirium (acute confusion), is a common, morbid, and costly complication of acute illness in older adults. Yet, researchers and clinicians lack short, efficient, and sensitive case identification tools for delirium. Though the Confusion Assessment Method (CAM) is the most widely used algorithm for delirium, the existing assessments that operationalize the CAM algorithm may be too long or complicated for routine clinical use. Item response theory (IRT) models help facilitate the development of short screening tools for use in clinical applications or research studies. This study utilizes IRT to identify a reduced set of optimally performing screening indicators for the four CAM features of delirium.
Older adults were screened for enrollment in a large scale delirium study conducted in Boston-area post-acute facilities (n = 4,598). Trained interviewers conducted a structured delirium assessment that culminated in rating the presence or absence of four features of delirium based on the CAM. A pool of 135 indicators from established cognitive testing and delirium assessment tools were assigned by an expert panel into two indicator sets per CAM feature representing (a) direct interview questions, including cognitive testing, and (b) interviewer observations. We used IRT models to identify the best items to screen for each feature of delirium.
We identified 10 dimensions and chose up to five indicators per dimension. Preference was given to items with peak psychometric information in the latent trait region relevant for screening for delirium. The final set of 48 indicators, derived from 39 items, maintains fidelity to clinical constructs of delirium and maximizes psychometric information relevant for screening.
We identified optimal indicators from a large item pool to screen for delirium. The selected indicators maintain fidelity to clinical constructs of delirium while maximizing psychometric information important for screening. This reduced item set facilitates development of short screening tools suitable for use in clinical applications or research studies. This study represents the first step in the establishment of an item bank for delirium screening with potential questions for clinical researchers to select from and tailor according to their research objectives.
谵妄（急性意识混乱）是老年人急性疾病的常见、严重且代价高昂的并发症。然而，研究人员和临床医生缺乏用于诊断谵妄的简短、高效且敏感的病例识别工具。尽管意识模糊评估法（CAM）是最广泛用于诊断谵妄的算法，但用于实施 CAM 算法的现有评估方法可能对于常规临床应用来说太长或太复杂。项目反应理论（IRT）模型有助于为临床应用或研究研究开发简短的筛选工具。本研究利用 IRT 来确定一组减少的最佳表现的筛选指标，用于诊断谵妄的四个 CAM 特征。
在波士顿地区的急性后设施中对年龄较大的成年人进行了大规模谵妄研究的入组筛选（n=4598）。经过培训的访谈者进行了一项结构化的谵妄评估，最终根据 CAM 对谵妄的四个特征的存在或不存在进行了评分。一个由 135 个来自既定认知测试和谵妄评估工具的指标组成的池，由一个专家小组按照每个 CAM 特征分为两个指标集进行分配，代表（a）直接访谈问题，包括认知测试，和（b）访谈者观察。我们使用 IRT 模型来确定用于筛查每个谵妄特征的最佳项目。
我们确定了 10 个维度，并为每个维度选择了最多五个指标。优先考虑在与筛查谵妄相关的潜在特征区域具有峰值心理测量信息的项目。最终的 48 个指标集，来自 39 个项目，保持了与谵妄临床结构的一致性，并最大限度地提高了与筛查相关的心理测量信息。
我们从一个大型项目库中确定了用于筛查谵妄的最佳指标。所选指标保持了与谵妄临床结构的一致性，同时最大限度地提高了与筛查相关的心理测量信息。这个简化的项目集便于开发适合临床应用或研究研究的简短筛选工具。本研究代表了建立谵妄筛查项目库的第一步，临床研究人员可以从中选择潜在的问题，并根据自己的研究目标进行调整。
