3D-CAM: derivation and validation of a 3-minute diagnostic interview for CAM-defined delirium: a cross-sectional diagnostic test study

Abstract

Background: Delirium is common, leads to other adverse outcomes, and is costly. However, it often remains unrecognized in most clinical settings. The Confusion Assessment Method (CAM) is the most widely used diagnostic algorithm, and operationalizing its features would be a substantial advance for clinical care.

Objective: To derive the 3D-CAM, a new 3-minute diagnostic assessment for CAM-defined delirium, and validate it against a clinical reference standard.

Design: Derivation and validation study.

Setting: 4 general medicine units in an academic medical center.

Participants: 201 inpatients aged 75 years or older.

Measurements: 20 items that best operationalized the 4 CAM diagnostic features were identified to create the 3D-CAM. For prospective validation, 3D-CAM assessments were administered by trained research assistants. Clinicians independently did an extensive assessment, including patient and family interviews and medical record reviews. These data were considered by an expert panel to determine the presence or absence of delirium and dementia (reference standard). The 3D-CAM delirium diagnosis was compared with the reference standard in all patients and subgroups with and without dementia.

Results: The 201 participants in the prospective validation study had a mean age of 84 years, and 28% had dementia. The expert panel identified 21% with delirium, 88% of whom had hypoactive or normal psychomotor features. Median administration time for the 3D-CAM was 3 minutes (interquartile range, 2 to 5 minutes), sensitivity was 95% (95% CI, 84% to 99%), and specificity was 94% (CI, 90% to 97%). The 3D-CAM did well in patients with dementia (sensitivity, 96% [CI, 82% to 100%]; specificity, 86% [CI, 67% to 96%]) and without dementia (sensitivity, 93% [CI, 66% to 100%]; specificity, 96% [CI, 91% to 99%]).

Limitation: Limited to single-center, cross-sectional, and medical patients only.

Conclusion: The 3D-CAM operationalizes the CAM algorithm using a 3-minute structured assessment with high sensitivity and specificity relative to a reference standard and could be an important tool for improving recognition of delirium.

Primary funding source: National Institute on Aging.

Conflict of interest statement

Declaration of conflicts: None declared—the authors retained full autonomy in carrying out this work.

Figures

Figure 1. Overview of 3D-CAM Assessment

This figure depicts the CAM diagnostic algorithm, with the 3D-CAM items and scoring summarized under each CAM diagnostic feature.

Figure 2. Prospective Validation Study Flow Diagram

*Presence or absence of delirium as determined by first 3D-CAM assessment was compared to the reference standard delirium diagnosis to assess validity, and to the second 3D-CAM assessment to assess inter-rater agreement All assessments were completed within 2 hours of each other, and results were strictly blinded from the other assessors