Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA.
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Rhode Island, Rhode Island, USA.
Dement Geriatr Cogn Disord. 2022;51(2):110-119. doi: 10.1159/000522522. Epub 2022 May 9.
The large number of heterogeneous instruments in active use for identification of delirium prevents direct comparison of studies and the ability to combine results. In a recent systematic review we performed, we recommended four commonly used and well-validated instruments and subsequently harmonized them using advanced psychometric methods to develop an item bank, the Delirium Item Bank (DEL-IB). The goal of the present study was to find optimal cut-points on four existing instruments and to demonstrate use of the DEL-IB to create new instruments.
We used a secondary analysis and simulation study based on data from three previous studies of hospitalized older adults (age 65+ years) in the USA, Ireland, and Belgium. The combined dataset included 600 participants, contributing 1,623 delirium assessments, and an overall incidence of delirium of about 22%. The measurements included the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnostic criteria for delirium, Confusion Assessment Method (long form and short form), Delirium Observation Screening Scale, Delirium Rating Scale-Revised-98 (total and severity scores), and Memorial Delirium Assessment Scale (MDAS).
We identified different cut-points for each existing instrument to optimize sensitivity or specificity, and compared instrument performance at each cut-point to the author-defined cut-point. For instance, the cut-point on the MDAS that maximizes both sensitivity and specificity was at a sum score of 6 yielding 89% sensitivity and 79% specificity. We then created four new example instruments (two short forms and two long forms) and evaluated their performance characteristics. In the first example short form instrument, the cut-point that maximizes sensitivity and specificity was at a sum score of 3 yielding 90% sensitivity, 81% specificity, 30% positive predictive value, and 99% negative predictive value.
DISCUSSION/CONCLUSION: We used the DEL-IB to better understand the psychometric performance of widely used delirium identification instruments and scorings, and also demonstrated its use to create new instruments. Ultimately, we hope that the DEL-IB might be used to create optimized delirium identification instruments and to spur the development of a unified approach to identify delirium.
目前有大量不同的仪器被用于识别谵妄，这使得研究之间无法直接比较，也无法对结果进行合并。在我们最近进行的一项系统评价中，我们推荐了四种常用且经过良好验证的工具，并使用先进的心理计量学方法对其进行了协调，以开发一个项目库，即谵妄项目库（DEL-IB）。本研究的目的是为现有的四种仪器找到最佳切点，并展示如何使用 DEL-IB 来创建新的仪器。
我们使用了二次分析和模拟研究，数据来自美国、爱尔兰和比利时的三项关于住院老年人（年龄≥65 岁）的研究。综合数据集包括 600 名参与者，共贡献了 1623 次谵妄评估，总体谵妄发生率约为 22%。测量包括精神障碍诊断与统计手册，第五版谵妄诊断标准、意识模糊评估方法（长表和短表）、谵妄观察筛查量表、修订版 98 项谵妄评定量表（总分和严重程度评分）和记忆谵妄评估量表。
我们为每种现有的仪器确定了不同的切点，以优化敏感性或特异性，并将每个切点的仪器性能与作者定义的切点进行了比较。例如，在 MDAS 上，使敏感性和特异性最大化的切点是总分 6 分，敏感性为 89%，特异性为 79%。然后，我们创建了四个新的示例仪器（两个短表和两个长表），并评估了它们的性能特征。在第一个示例短表仪器中，使敏感性和特异性最大化的切点是总分 3 分，敏感性为 90%，特异性为 81%，阳性预测值为 30%，阴性预测值为 99%。
讨论/结论：我们使用 DEL-IB 来更好地理解广泛使用的谵妄识别工具和评分的心理计量学性能，同时也展示了它在创建新工具方面的应用。最终，我们希望 DEL-IB 可以用于创建优化的谵妄识别工具，并推动制定一种统一的方法来识别谵妄。
