3D-CAM Accurately Identifies Delirium in Patients
A modified 3D-Confusion Assessment Method can identify CAM-defined delirium in three minutes.
The 3D-CAM can diagnose delirium in just 3 minutes.
A new tool, 3D-CAM, can accurately diagnose delirium in three minutes and performed comparably to clinical reference standards, according to study findings published in the Annals of Internal Medicine.
The Confusion Assessment Method (CAM) is the most widely-used diagnostic algorithm for delirium, but the condition often goes unrecognized in many clinical settings. So Edward R. Marcantonio, MD, of the Beth Israel Deaconess Medical Center, and colleagues sought to optimize the CAM to make it easier to correctly diagnose delirium.
They identified 20 items that best operationalized the four CAM diagnostic features in order to create the 3D-CAM. To test their system, the trained research assistants administered 3D-CAM assessments to 201 inpatients aged 75 years and older.
Clinicians then independently performed extensive assessments of each patient, including interviews and medical record reviews. An expert panel looked at the data to determine whether delirium and/or dementia were present. Findings from the 3D-CAM delirium diagnosis were then compared with the reference standard.
Among the 201 participants, 28% had dementia and 21% had delirium. It took an average of 3 minutes to administer the 3D-Cam.
The 3D-CAM's sensitivity was 95% (95% CI: 84%-99%), and specificity was 94% (95% CI: 90%-97%). Among those with dementia 3D-CAM's sensitivity was 96% (95% CI: 82%-100%), and specificity was 86% (95% CI: 67% - 96%). Among patients without dementia sensitivity was 93% (95% CI: 66%-100%], and specificity was 96% (95% CI: 91% to 99%).
These results indicate that the 3D-CAM could be an important tool for improving recognition of delirium, according to the researchers.