Diagnosing delirium in older hospitalized adults with dementia: adapting the confusion assessment method to international classification of diseases, tenth revision, diagnostic criteria

Abstract

Objectives: To compare performance characteristics of the Confusion Assessment Method (CAM) algorithm for screening and delirium diagnosis with criteria for delirium from the International Classification of Diseases, Tenth Revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) in high-risk individuals.

Design: Prospective cohort study.

Setting: Academic geriatric hospital.

Participants: One hundred two individuals aged 80 to 100 hospitalized for acute medical illness.

Measurements: Complete CAM instrument (nine items), scored using the four-item CAM diagnostic algorithm. Criterion standard classification of delirium was rated independently according to expert consensus based on DSM-IV and ICD-10 criteria for delirium.

Results: In 79 hospitalized participants, the CAM performed well for delirium screening (delirium prevalence of 24% according to DSM-IV and 14% according to ICD-10). Of all CAM features, acute onset and fluctuating course are most important for diagnosis (area under the receiver operating characteristic curve (AUC) = 0.92 in DSM-IV and 0.83 in ICD-10). The CAM diagnostic algorithm had a sensitivity of 0.74, a specificity of 1.0, and an AUC of 0.88 compared with the DSM-IV reference standard and a sensitivity of 0.82, a specificity of 0.91, and an AUC of 0.85 compared with the ICD-10. Compared with the ICD-10, adding psychomotor change to the CAM algorithm improved specificity to 97%, but sensitivity fell to 55% (AUC = 0.96). Applying psychomotor change sequentially only to the group that the CAM algorithm identified as having no delirium improved sensitivity to 91% with specificity of 85% (AUC = 0.95).

Conclusion: Although the CAM diagnostic algorithm performed well against a DSM-IV reference standard, adding psychomotor change to the CAM algorithm improved specificity and diagnostic value against ICD-10 criteria overall in older adults with dementia and improved sensitivity and screening performance when applied sequentially in CAM-negative individuals.

Figure 1. Diagnostic Validity of the CAM Algorithm, Sequential and Additive Model for ICD-10 Delirium Diagnosis

Original 4-item CAM algorithm (DSM-based), sequential application (original CAM algorithm, with sequential addition of the psychomotor change item only to the CAM-negative group) and additive model i.e., adding psychomotor change to the original CAM algorithm are depicted. All models are validated against the ICD-10 delirium diagnosis as reference standard. The area under the ROC curve adapted with maximum likelihood method is depicted. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) as well as correct classification percentages are given.