Assessment of Delirium in Hospitalized Patients

Delirium Clinical Trial

Official title:

Objective Assessment of Delirium in Hospitalized Patients

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT02258386
• Other study ID #: IRB #1409472204
• Status: Not yet recruiting
• Phase: N/A
• First received: October 2, 2014
• Last updated: October 2, 2014
• Start date: October 2014
• Est. completion date: September 2017

Study information

• Verified date: October 2014
• Source: University of Arizona
• Contact: Jane Mohler, PhD
• Phone: 520-626-5807
• Email: jmohler[@]aging.arizona.edu
• Is FDA regulated: No
• Health authority: United States: Institutional Review Board
• Study type: Observational

Clinical Trial Summary

The aim of the present study is to implement a novel body-worn sensor, the ZephyrTM BioModule (the registered name for the wearable sensor device used in our study), for assessing physical activity and physiological measures in a clinical setting together with other clinical routine assessments. Our specific aim is to explore whether and how specific physical activity pattern and physiological measures are associated with delirium in hospital inpatient. Our ultimate goal is early detection and intervention of delirium, and better management through monitoring of activity pattern and physiological information. The ZephyrTM a U.S. FDA-approved wireless, ambulatory physiological monitoring device that consists of a data logger and transmitter unit which is attached at the location of the 5th intercostal space using a specifically designed patch (BioPatchTM) and standard ECG electrodes. The ZephyrTM is designed for long-term measurement of a patient's physical activity and physiological measures. By using the physical activity parameters and physiological measures, a specific delirium risk profile will be computed for each patient. Finally, the association of this delirium risk profile with CAM-ICU questionnaire as a gold standard will be assessed.

Description:

After obtaining written informed consent and Protected Health Information (PHI) consent, CAM-ICU (Confusion Assessment Method for the ICU) will be measured routinely by unit nurses each shift. Results will be available in the medical record. We will then measure physical activity and physiological measures (e.g. ECG, heart rate, respiration rate, and body temperature) using an unobtrusive body worn sensor. This sensor will be attached to the 5th intercostal space using a specifically designed patch (BioPatchTM) to allow quantification of physical activity such as walking, standing, sitting, lying, as well as measurement of other physiological parameters. These measurements will only be available to the research team and will not be available to the nurses on the inpatient study unit. Medical history (from clinic chart) and demographic information will be obtained, such as age, gender, weight, and height as well as details (e.g. injuries, fall score and number of falls) which happened during hospital stay. The research tests consist of one core measurements and one ancillary measurements (as required) as listed below (Note - ancillary measurement may not be required for everyone).

Core measurements Assessment of physical activity and physiological measures: Patients will be asked to wear the Zephyr BioModule for a period of 24 hours to maximum stay in the hospital. The Zephyr BioModule will be attached to the patients to the 5th intercostal space using a specifically designed patch (BioPatchTM).

Ancillary Measurements Upper Extremity Test: A validated body worn sensors (LEGSys™, Biosensics LLC) will be used to evaluate upper extremity kinematics (angular velocities). This is a simple test where the patient flexes and extends their arm for 20 seconds on each arm.

Analysis plan:

To predict the risk of delirium using physical activity and physiological data, each patient participating in the study will be categorized as to presence of delirium based on CAM-ICU questionnaire. Logistic regression analysis will be used to examine the relation between each physical activity variable (e.g. lying time) and physiological measures (e.g. heart rate) with delirium risk. Firstly, univariate logistic regression, using presence of delirium as the dependent variable, will be employed to investigate the relationship of the test variables. The Odd Ratios (OR) and coefficient of determination (R2) will be calculated for each explanatory variable. Then multivariate logistic regression using stepwise-automated methods will be performed to investigate the independent effects of the physical activity variables and physiological measures in predicting delirium. Sensitivities, specificities, positive and negative predictive values of different cut-off values will be calculated for any non-categorical variable shown to have an independent effect on predicting delirium. A two-sided P-value ≤0.05 will be considered to be statistically significant. Statistical analysis will be performed using SPSS (Statistical Package for the Social Sciences) statistics 22.0 (IBM, Armonk (name of town), NY (New York), USA).

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 200
• Est. completion date: September 2017
• Est. primary completion date: September 2015
• Accepts healthy volunteers: No
• Gender: Both
• Age group: 70 Years and older

Eligibility

Inclusion Criteria: At least one of the following:

• Cognitive impairment,

• UTI (urinary tract infection)

• Pneumonia,

• Status post hip fracture or trauma,

->5 medications,

• Previous hospitalization in past 6 months

Exclusion Criteria:

• Refusal to participate

• Terminally ill

• On ventilator

• Delirium based on CAM-ICU

Study Design

Time Perspective: Prospective

Related Conditions & MeSH terms

• Delirium

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
University of Arizona

References & Publications (10)

Albert MS, Levkoff SE, Reilly C, Liptzin B, Pilgrim D, Cleary PD, Evans D, Rowe JW. The delirium symptom interview: an interview for the detection of delirium symptoms in hospitalized patients. J Geriatr Psychiatry Neurol. 1992 Jan-Mar;5(1):14-21. — View Citation

Cole MG. Delirium in elderly patients. Am J Geriatr Psychiatry. 2004 Jan-Feb;12(1):7-21. Review. — View Citation

Foreman MD, Wakefield B, Culp K, Milisen K. Delirium in elderly patients: an overview of the state of the science. J Gerontol Nurs. 2001 Apr;27(4):12-20. Review. — View Citation

Francis J. Delirium in Older Patients. J Am Geriatr Soc. 1992;40:829-38. State-of-the-Art review which includes a comprehensive reference list.

Gleason OC. Delirium. Am Fam Physician. 2003 Mar 1;67(5):1027-34. Review. — View Citation

Inouye SK. The dilemma of delirium: clinical and research controversies regarding diagnosis and evaluation of delirium in hospitalized elderly medical patients. Am J Med. 1994 Sep;97(3):278-88. Review. — View Citation

Leslie DL, Zhang Y, Holford TR, Bogardus ST, Leo-Summers LS, Inouye SK. Premature death associated with delirium at 1-year follow-up. Arch Intern Med. 2005 Jul 25;165(14):1657-62. — View Citation

McCusker J, Cole M, Dendukuri N, Belzile E, Primeau F. Delirium in older medical inpatients and subsequent cognitive and functional status: a prospective study. CMAJ. 2001 Sep 4;165(5):575-83. — View Citation

O'Keeffe S, Lavan J. The prognostic significance of delirium in older hospital patients. J Am Geriatr Soc. 1997 Feb;45(2):174-8. — View Citation

Trzepacz PT, Baker RW, Greenhouse J. A symptom rating scale for delirium. Psychiatry Res. 1988;23:89-97. Presents the Delirium Rating Scale (DRS), a detailed clinical scale designed to be completed after psychiatric assessment.

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Objective Assessment of Delirium in Hospitalized Patients	To identify biodata (physiological measurement) patterns indicative of the 3 subtypes of delirium (hypoactive, hyperactive and mixed). To build an algorithm sensitive and specific to the diagnosis of each of the three subtypes of delirium for future integration into a delirium monitoring system capable of objective delirium diagnosis, concentrating on those patterns indicative of early identification.	3 years	No