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Early Warning System for Clinical Deterioration on General Hospital Wards

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ClinicalTrials.gov Identifier: NCT01280942
Recruitment Status : Completed
First Posted : January 21, 2011
Last Update Posted : July 27, 2012
Information provided by (Responsible Party):

Study Description
Brief Summary:
The goal is to develop a two-tiered monitoring system to improve the care of patients at risk for clinical deterioration on general hospital wards (GHWs) at Barnes-Jewish Hospital (BJH). The investigators hypothesize that the use of an automated early warning system (EWS) that identifies patients at risk of clinical deterioration, with notification of nurses on the GHWs when patients are identified, will reduce the risk of ICU transfer or death within 24 hrs of an alert. As a substudy, the investigators will pilot the use of a wireless pulse oximeter to establish feasibility and to develop algorithms for a real-time event detection system (RDS) in these high-risk patients.

Condition or disease Intervention/treatment
Escalation of Care Cardiopulmonary Arrest Respiratory Arrest Severe Sepsis Septic Shock Behavioral: EWS Nursing Alerts Device: Wireless Remote Sensor

Study Design

Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 20031 participants
Allocation: Randomized
Intervention Model: Crossover Assignment
Masking: None (Open Label)
Primary Purpose: Prevention
Official Title: Early Warning System for Clinical Deterioration on General Hospital Wards.
Study Start Date : January 2011
Primary Completion Date : May 2012
Study Completion Date : May 2012

Resource links provided by the National Library of Medicine

U.S. FDA Resources

Arms and Interventions

Arm Intervention/treatment
No Intervention: Control
Patients admitted to 4 GHWs designated as controls. Nurses will not be notified when patients on these GHWs satisfy the EWS algorithm. They will also not wear the wireless sensor devices.
Experimental: Nurse notification of EWS alert
Patients admitted to 4 GHWs designated as intervention wards at BJH. Nurses will be notified when patients on these wards satisfy the EWS algorithm. Some patients will be asked to wear the wireless remote sensors.
Behavioral: EWS Nursing Alerts
An automated algorithm (EWS) will identify patients at potential risk of clinical deterioration. When a patient satisfies the algorithm, a nurse on the patient's ward will be notified. S/he will assess the patient and institute any interventions that are clinically required.
Device: Wireless Remote Sensor
A subset of patients will be consented to wear a wireless sensor device which will monitor heart rate and level of oxygen in the blood.

Outcome Measures

Primary Outcome Measures :
  1. Transfer to ICU or unexpected death within 24 hrs of identification by the EWS algorithm [ Time Frame: Within 24 hrs of an EWS alert ]
    The proportion of patients transferred to ICU, and the time from EWS detection to ICU transfer in patients, for intervention and control wards.

Secondary Outcome Measures :
  1. Clinical outcomes and process measures [ Time Frame: Hospital discharge (avg = 3 days) ]
    Processes of care (fluid rescuscitation, escalation of antibiotics), length of stay, acquired organ dysfunction, and causes of CPR or ICU transfer

Eligibility Criteria

Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No

Inclusion Criteria:

  • All patients age 18 and above, hospitalized in GHWs at Barnes Jewish Hospital.

Exclusion Criteria:

  • Minors, patients younger than 18 years old.
Contacts and Locations

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To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01280942

United States, Missouri
Barnes Jewish Hospital
St Louis, Missouri, United States, 63108
Sponsors and Collaborators
Tom Bailey, MD
Principal Investigator: Thomas C Bailey, MD Washington University School of Medicine
More Information

Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):
Responsible Party: Tom Bailey, MD, Professor of Medicine, Washington University
ClinicalTrials.gov Identifier: NCT01280942     History of Changes
Other Study ID Numbers: 10-0514
7322-01 ( Other Grant/Funding Number: BJH Foundation/ICTS )
First Posted: January 21, 2011    Key Record Dates
Last Update Posted: July 27, 2012
Last Verified: July 2012

Keywords provided by Tom Bailey, MD, Washington University:
clinical deterioration
detection algorithms
automated warning systems

Additional relevant MeSH terms:
Heart Arrest
Shock, Septic
Heart Diseases
Cardiovascular Diseases
Systemic Inflammatory Response Syndrome
Pathologic Processes
Respiration Disorders
Respiratory Tract Diseases
Signs and Symptoms, Respiratory
Signs and Symptoms