Using a systematic, well-described qualitative methodology, a panel of seven interdisciplinary clinical experts achieved outstanding consensus in the assignment of standard delirium assessment items to key delirium features of the CAM diagnostic algorithm. A comprehensive and a brief list of indicators of key features of delirium were created. The delirium indicators include patient interview questions, cognitive tasks, and interviewer observations—each representing an important assessment modality for the evaluation of delirium. By providing specific assessment items for each CAM feature, our results may provide a valuable guide for healthcare professionals performing clinical assessments for delirium.
The panel determined that Feature 1 (Acute Change or Fluctuating Course) can be indicated, not surprisingly, by observed fluctuation of mental status domains but may also be informed by evidence of acute temporal, spatial, and personal disorientation, confusion, or perceptual disturbances. Feature 2 (Inattention) is indicated by errors on cognitive tasks designed to evaluate attention, and by observations of decreased awareness of surroundings (e.g. staring into space, losing track of interview) or a hyper-vigilant state (e.g. easily distracted). Of note, many indicators of Feature 2 are characterized by a response of “don’t know” or no response from the patient, suggesting that often a pattern of lack of response, rather than the specific question content, indicates the presence of inattention. Indicators of Feature 3 (Disorganized Thinking) include abnormalities of thought (e.g. illogical ideas, paucity of thought) or speech (e.g. disjointed words or phrases) as well as evidence of temporal, spatial, and personal disorientation, confusion, or perceptual disturbances. Feature 4 (Altered Level of Consciousness) is indicated by observations of decreased (e.g. sleepy, lethargic) or increased level of consciousness (e.g. easily startled) and a pattern of unresponsiveness to questions, which the subpanel added to the shortened list of indicators as its own summary indicator.
Some results of the item assignments deserve further comment. First, a large number of indicators in do not imply increased usefulness in delirium assessment; it simply reflects a large number of items with related content that were selected for the initial pool of assessment items. For example, the DSI contained numerous items describing the manifestations of perceptual disturbances, accounting for the large number of perceptual disturbance indicators in . However, perceptual disturbances are relatively infrequent and poorly sensitive for delirium detection (sensitivity 19–30%).15
Second, consensus opinions from this study suggest that the type of response, in particular “don’t know” or no response, may provide information in addition to the actual question content. Thus, for the creation of future delirium instruments, explicit recording of no response and “don’t know” responses may be useful.
Previous studies have shown that variations in the use of formal cognitive testing and interpretation of interviewer observations affect the performance of the CAM, yet not all healthcare professionals have the training required for its optimal application.8,17,18
By listing specific assessment items, including cognitive tasks, that can be used to evaluate each delirium feature, our results will assist clinicians in the more effective implementation of the CAM diagnostic algorithm. In addition, the interviewer observations identified by our process may also help healthcare professionals put their observations, made either during routine clinical care or specific mental status evaluation, into the context of delirium symptoms. Identification of positive CAM features using these indicators should prompt a formal evaluation by an expert clinician and, if confirmed, appropriate steps to identify and address the underlying cause of delirium.
Strengths of this study include the use of a systematic, well-described qualitative methodology, the wide range of expertise represented by the panel, and the high level of consensus achieved after the panel meeting. Other strengths include a framework of key delirium features that will be easy to use in the clinical setting, a list of specific assessment items to guide the evaluation of each CAM feature, the inclusion of multiple assessment modalities, and the integration with the CAM diagnostic algorithm to enhance delirium recognition at the bedside.
Some limitations to this study must be noted. First, our initial item pool does not contain every neuropsychological test that can be used to assess delirium. However, it does contain tests of temporal, spatial, and personal orientation, many standard tests of attention, and commonly used tests for all pertinent domains of delirium. Secondly, items were frequently assigned to multiple CAM features in the comprehensive list of indicators. This finding reflects the inter-related nature of the CAM features and the challenge of isolating mental status domains in neuropsychological testing. The initial methodological decision to not restrict the number of CAM features to which each item could be assigned contributed to these multiple assignments. The subpanel took steps to limit multiple assignments in the brief list of indicators. Lastly, this study does not utilize any patient data, and the panel decisions represent consensus of expert opinions, not objective measures. However, the expert panel provided invaluable insights into this complex syndrome, allowing us to advance understanding in this area. We will take steps to validate this list of indicators using clinical data and to identify the best-performing assessment items for each delirium feature using advanced measurement techniques. The long-term goal is to use these indicators to create a brief delirium screening instrument for the clinical setting.
Prompt recognition of delirium and timely intervention may reduce its morbidity and mortality,5,6
yet its multifaceted nature makes diagnosis challenging, especially for those untrained in delirium and cognitive assessments.14
We have created both a comprehensive and a brief list of indicators for delirium based on the key delirium features in the CAM diagnostic algorithm to provide a framework for delirium assessment. By listing specific assessment items, response patterns, and linking these to the key CAM features, our results may be useful for helping healthcare professionals recognize delirium at the bedside. In addition, this work lays the groundwork for the creation of future brief screening instruments to improve delirium detection.