OBJECTIVES: To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium. DESIGN: Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm. SETTING: Meetings of expert panel. PARTICIPANTS: Panel of seven interdisciplinary clinical experts. MEASUREMENTS: Panelists' assignments of each assessment item to indicate CAM features. RESULTS: 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. CONCLUSION: 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.
OBJECTIVES: To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium. DESIGN: Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm. SETTING: Meetings of expert panel. PARTICIPANTS: Panel of seven interdisciplinary clinical experts. MEASUREMENTS: Panelists' assignments of each assessment item to indicate CAM features. RESULTS: 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. CONCLUSION: 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.
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