Aging Brain Center, Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA.
J Am Geriatr Soc. 2012 Jun;60(6):1044-50. doi: 10.1111/j.1532-5415.2012.03996.x.
To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium.
Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm.
Meetings of expert panel.
Panel of seven interdisciplinary clinical experts.
Panelists' assignments of each assessment item to indicate CAM features.
From an initial pool of 119 assessment items, the panel assigned 66 items to at least one CAM feature, and many items were assigned to more than one feature. Experts achieved a high level of consensus, with a postmeeting kappa for agreement of 0.98. The study staff compiled the assignment results to create a comprehensive list of CAM feature indicators, consisting of 107 patient interview questions, cognitive tasks, and interviewer observations, with some items assigned to multiple features. A subpanel shortened this list to 28 indicators of important delirium features.
A systematic, well-described qualitative methodology was used to create a list of indicators for delirium based on the features of the CAM diagnostic algorithm. This indicator list may be useful as a clinical tool for enhancing delirium recognition at the bedside and for aiding in the development of a brief delirium screening instrument.
采用专家共识流程来确定谵妄特征的指标，以帮助提高床边对谵妄的识别能力。
修改后的 Delphi 共识流程，将现有的认知和谵妄评估项目分配到谵妄评估方法（CAM）诊断算法的谵妄特征中。
专家小组会议。
七位跨学科临床专家组成的小组。
小组成员对每个评估项目的分配，以指示 CAM 特征。
从最初的 119 个评估项目中，小组将 66 个项目分配给至少一个 CAM 特征，许多项目被分配给多个特征。专家们达成了高度共识，会后的kappa 一致性为 0.98。研究人员将分配结果汇编成一个全面的 CAM 特征指标列表，包括 107 个患者访谈问题、认知任务和访谈者观察，其中一些项目被分配给多个特征。一个子小组将该列表缩短至 28 个重要谵妄特征的指标。
采用系统、描述良好的定性方法，根据 CAM 诊断算法的特征创建了一个谵妄指标列表。该指标列表可作为一种临床工具，有助于提高床边对谵妄的识别能力，并有助于开发简短的谵妄筛查工具。
