UB-CAM Delirium Screen

Introduction Delirium in hospitalized older adults is associated with higher rates of falls and other nosocomial complications, longer length of hospital stay, declines in physical and cognitive function, increased care costs, nursing home placement, and mortality.1–4 Despite these poor outcomes, less than half of all delirium cases are detected during routine hospital care.2 Older age, presence of dementia, and hypoactive delirium are previously identified risk factors for non-recognition.1 Undetected delirium results in distress for the older adult and family, increased staff burden and stress, and a missed opportunity to intervene by assessing and managing the underlying causes.2,4,5 To address the challenge of delirium identification, our team developed, validated, and field tested the Ultra-brief Confusion Assessment Method (UB-CAM), a two-step delirium identification protocol that includes an ultra-brief 2-item screen (UB-2) paired with a validated diagnostic tool (3D-CAM). If hospitalized older adults answer both UB-2 questions correctly (day of week and months of the year backwards), the protocol ends and delirium is not present; otherwise, additional questions from the 3D-CAM are administered to determine the presence or absence of the 4 CAM diagnostic features.6 To promote ease of bedside use and integration into clinician workflow,7 we previously developed, tested, and implemented a UB-CAM app using the Application Programming Interface (API) with the Research Electronic Data Capture (REDCap).8 To further improve accessibility and sustainability in the clinical setting, our research team collaborated with a computer scientist to develop and refine an iOS-based UB-CAM app for the iPhone and iPad. Through iterative testing we refined the app to include explicit written instructions and color cueing to enhance the flow of screening (Figure 1). Also, the new iOS app included a feature that allows screening results to be saved to facilitate subsequent assessment of CAM Feature 1: Acute Change. The aim of this report is to describe field-testing of the app on an iPhone at the bedside with patients. Methods We used the new iOS app to conduct UB-CAM assessments with consented medical patients enrolled in the Family-centered Function-focused Care (Fam-FFC) cluster randomized trial, described elsewhere.9 Trained research assistants (RAs) collected the data within 48 hours of admission in a large academic medical center in central Pennsylvania. Similar to our previous app, the iOS App was linked to REDCap via an API that sent and received data from/to the app. The app stored previous days' assessments from the same patient, and automatically checked for "acute change" by comparing the current day assessment with previous results. For patients screening positive for delirium, the assessor could query the app for workup and management recommendations. The data from REDCap were sent to the SAS statistical analysis system for analysis. We recorded the amount of time it took to conduct the assessment on the app. Inter-rater reliability was tested in paired screenings with RAs. We also solicited qualitative feedback from RAs assessing app acceptability and efficiency. This study was approved by the university institutional review board. Results Eleven older adults ( ???? x =83 years, SD=8.0), 6 women (55%), and all with mild dementia upon hospital admission (Montreal Cognitive Assessment ???? x =14.2, SD=7.5; Clinical Dementia Rating Scale ???? x =1.1, SD=0.3) were enrolled (Table 1). Researchers conducted 42 app-directed UB-CAM assessments and 27 (64%) were delirium positive. Administration of the iOS UB-CAM app took 90 seconds (SD=8 secs) on average. Of the 42 assessments, 18 were conducted in parallel between 2 raters and there was 100% agreement on the presence of delirium. All raters reported high acceptability (100% satisfaction) including describing the app as easy to use and appreciated the ability to obtain acute change from the stored previous day's results. Features described as positive included the immediate screening outcomes (delirium present/absent) and the display of treatment options after a positive screen. Discussion We provide early evidence that our new iOS-based UB-CAM app demonstrates high efficiency, inter-rater reliability, and acceptability, suggesting that the app will effectively support implementation of the validated UB-CAM two-step protocol. The feature of daily tracking helps identify changes in condition and the display of next steps for workup, and management of delirium after a positive screen promotes timely interventions. Next steps include testing effectiveness in a pragmatic trial with clinician users (physicians, nurses, certified nursing assistants), integrating the UB-CAM app into routine hospital care for all older patients, including those with dementia. Having rapid, accurate bedside delirium detection that is easily integrated into clinician workflow and co-designed with stakeholder input has the potential to transform care.

Systematic screening improves delirium identification among hospitalized older adults. Little data exists on how to implement such screening. Objective: To test implementation of a brief app-directed delirium identification protocol by physicians, nurses, and certified nursing assistants (CNAs) in real-world practice relative to a research reference standard delirium assessment (RSDA). Design: Prospective diagnostic test study Setting: Large urban academic medical center, small rural community hospital Participants: 527 general medicine inpatients (mean age 80 years, 35% with pre-existing dementia); 399 clinicians (53 physicians, 236 nurses, 110 CNAs) Measurements: On two study days, enrolled patients underwent an RSDA. Subsequently, CNAs performed an ultra-brief delirium screen (UB-2), while physicians and nurses performed a two-step protocol consisting of the UB-2, followed in "positives" by the 3D-CAM diagnostic assessment. Results: Delirium was diagnosed in 154/924 RSDAs (17%) and in 114/527 patients (22%). Completion rate for clinician protocols exceeded 97%. The UB-2 was administered in 62 (51) [mean (SD)] seconds by CNAs; two-step protocols were administered in 104 (99) seconds by nurses, and 106 (105) seconds by physicians. The UB-2 had sensitivities (95% CI) of 88 (72–96)%, 87 (73–95)%, and 82 (65–91)% when administered by CNAs, nurses, and physicians, respectively, with specificities of 64–70%. The two-step protocol had overall accuracy of 89 (83–93)% and 87 (81–91)%, with sensitivities of 65 (48–79)% and 63 (46–77)%, and specificities of 93 (88–96)% and 91 (86–95)% for nurses and physicians, respectively. Two-step sensitivity for moderate-severe delirium was 78 (54–91)%. Limitations: Two sites, limited diversity Conclusion: An app-directed delirium identification protocol was feasible, brief, and accurate, with CNAs and nurses performing as well as physicians.