Abstract
Recently, the Ultra-Brief Confusion Assessment Method (UB-CAM), designed to help physicians and nurses to recognize delirium, showed high, but imperfect, accuracy compared with Research Reference Standard Delirium Assessments (RRSDAs). The aim of this study is to identify factors associated with disagreement between clinicians' app-based UB-CAM assessments and RRSDAs. This is a secondary analysis of a prospective diagnostic test study. The study was conducted at two hospitals and included 527 inpatients (≥70 years old) and 289 clinicians (53 physicians, 236 nurses). Trained research associates performed RRSDAs and determined delirium presence using the CAM. Clinicians administered the UB-CAM using an iPad app. Disagreement factors considered were clinician, patient, and delirium characteristics. We report odds ratios and 95% confidence intervals. One thousand seven hundred and ninety-five clinician UB-CAM assessments paired with RRSDAs were administered. The prevalence of delirium was 17%. The rate of disagreement between clinician UB-CAM assessments and RRSDAs was 12%. Significant factors associated with disagreement between clinician UB-CAM assessments and RRSDAs (OR [95% CI]) included: presence of dementia (2.7 [1.8-4.1]), patient education high school or less (1.9 [1.3-2.9]), psychomotor retardation (2.5 [1.4-4.2]), and the presence of mild delirium or subsyndromal delirium (5.5 [3.5-8.7]). Significant risk factors for false negatives were patient age less than 80 (2.2 [1.1-4.3]) and mild delirium (3.5 [1.6-7.4]). Significant risk factors for false positives were presence of dementia (4.0 [2.3-7.0]), subsyndromal delirium (5.1 [2.9-9.1]), and patient education high school or less (2.0 [1.2-3.6]). Clinician characteristics were not significantly associated with disagreement. The strongest factors associated with disagreement between clinician UB-CAM screens and RRSDAs were the presence of dementia and subsyndromal delirium as risk factors for false positives, and mild delirium and younger age as a risk factor for false negatives. These disagreement factors contrast with previous studies of risk factors for incorrect clinician delirium screening, and better align screening results with patient outcomes.
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