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Comparing an Automated to a Conventional Sepsis Clinical Prediction Rule

This study has been withdrawn prior to enrollment.
Sponsor:
ClinicalTrials.gov Identifier:
NCT01505478
First Posted: January 6, 2012
Last Update Posted: April 5, 2017
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
Collaborators:
New York University
United States Department of Defense
Information provided by (Responsible Party):
Steven Horng, MD, Beth Israel Deaconess Medical Center
  Purpose
The investigators will conduct a prospective cohort study to compare an automated sepsis severity score to a conventional clinical prediction rule to risk stratify patients admitted from the emergency department (ED) with suspected infection for 28 day in-hospital mortality.

Condition
Sepsis

Study Type: Observational
Study Design: Observational Model: Cohort
Time Perspective: Prospective
Official Title: Comparing an Automated to a Conventional Sepsis Clinical Prediction Rule

Resource links provided by NLM:


Further study details as provided by Steven Horng, MD, Beth Israel Deaconess Medical Center:

Primary Outcome Measures:
  • 28 day in-hospital mortality
    The primary endpoint is the AUC of a model to predict 28 day all cause in-hospital mortality. Patients discharged or transferred to another hospital before 28 days will be assumed to be alive at 28 days.


Secondary Outcome Measures:
  • ICU Admission
    The secondary endpoint is ICU admission from the ED or within 24 hours from the floor.


Enrollment: 0
Study Start Date: July 2012
Groups/Cohorts
Patients admitted with infection
All consecutive ED patients during the study period that have been admitted and identified to have a suspected infection at ED disposition using a data collection tool

  Eligibility

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
Sampling Method:   Non-Probability Sample
Study Population
Patients admitted from the Emergency Department with suspected infection.
Criteria

Inclusion Criteria:

  • All consecutive adult (age 18 or older) Emergency Department (ED) patients during the study period that have been admitted from the ED and identified by the treating clinician to have a suspected infection at the time of ED disposition will comprise our study population.

Exclusion Criteria:

  • No patients will be excluded from the study.
  Contacts and Locations
Information from the National Library of Medicine

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): NCT01505478


Locations
United States, Massachusetts
Beth Israel Deaconess Medical Center
Boston, Massachusetts, United States, 02215
Sponsors and Collaborators
Beth Israel Deaconess Medical Center
New York University
United States Department of Defense
Investigators
Principal Investigator: Steven Horng, MD Beth Israel Deaconess Medical Center
  More Information

Responsible Party: Steven Horng, MD, Instructor in Emergency Medicine, Beth Israel Deaconess Medical Center
ClinicalTrials.gov Identifier: NCT01505478     History of Changes
Other Study ID Numbers: 2011P000356
First Submitted: January 4, 2012
First Posted: January 6, 2012
Last Update Posted: April 5, 2017
Last Verified: January 2012

Keywords provided by Steven Horng, MD, Beth Israel Deaconess Medical Center:
Sepsis
Clinical Prediction Rule
Clinical Informatics
Machine Learning

Additional relevant MeSH terms:
Sepsis
Toxemia
Infection
Systemic Inflammatory Response Syndrome
Inflammation
Pathologic Processes