Abstract Details

Title Detection of Delirium in Hospitalized Patients Using Video-monitoring

Topic Neurohospitalist

Presentation(s) P10 - Poster Session 10 (8:00 AM-9:00 AM)

Poster/Presentation
Number 2-011

Objective This study aimed to use video monitoring to identify patient behaviors and environmental characteristics associated with delirium to develop efficient markers of delirium.

Background Delirium is an acute confusional state common in older hospitalized patients, associated with increased morbidity and mortality. However, delirium is under-recognized. Current clinical tools to assess delirium are subjective, require in-person evaluation, and can be time-consuming. There is a need for methods to improve detection and prediction of delirium that could be applied efficiently and broadly.

Design/Methods We conducted an observational cohort study of hospitalized patients expected to stay at least one night, who were evaluated up to twice daily using video followed by bedside evaluations. Video evaluations were performed using structured observation of patient behaviors and environmental features via the hospital’s real-time video monitoring system, visualized outside of patients’ rooms. Bedside evaluations for delirium were performed using the 3-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM). Pearson’s chi-squared test and generalized linear mixed-effects models were used to identify features differentiating delirious and non-delirious patients.

Results A total of 318 video evaluations (46 unique patients) were analyzed. Among these video evaluations, 199 corresponded with a positive delirium assessment (median video evaluation duration 2.05 minutes), and 119 with no delirium (median video evaluation duration 1.60 minutes). Analysis revealed video features significantly associated with delirium, including reduced spontaneous and purposeful movements, eyes either closed or fixed in a stare, mouth being open, and less interaction with the environment (ex. other people, phone, TV).

Conclusions

Distinct behaviors and environmental interactions observable via video may help differentiate patients with and without delirium. Recognition of these features may improve clinician bedside assessment. Objective video-monitored features that can be viewed remotely may serve as markers to improve delirium detection.

Authors/Disclosures
Sahithi Gangavarapu PRESENTER	Ms. Gangavarapu has nothing to disclose.
Shreya Dhar	Ms. Dhar has received personal compensation for serving as an employee of Northwestern University .
Catherine Zhao	Ms. Zhao has nothing to disclose.
Ian G. Sherrington	Mr. Sherrington has nothing to disclose.
Eyal Y. Kimchi, MD, PhD (Northwestern University)	The institution of Dr. Kimchi has received research support from NIH. The institution of an immediate family member of Dr. Kimchi has received research support from NIH.

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