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Predictive algoRithm for EValuation and Intervention in SEpsis (PREVISE)

Primary Purpose

Sepsis, Septic Shock, Severe Sepsis

Status
Completed
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
Severe Sepsis Prediction
Severe Sepsis Detection
Sponsored by
Dascena
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Sepsis

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

  • All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible.

Exclusion Criteria:

  • All patients younger than 18 years of age will be excluded.

Sites / Locations

  • Cabell Huntington Hospital

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

With InSight

Without Insight

Arm Description

Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.

Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.

Outcomes

Primary Outcome Measures

In-hospital mortality

Secondary Outcome Measures

Hospital length of stay

Full Information

First Posted
July 27, 2017
Last Updated
September 17, 2021
Sponsor
Dascena
Collaborators
Cabell Huntington Hospital
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1. Study Identification

Unique Protocol Identification Number
NCT03235193
Brief Title
Predictive algoRithm for EValuation and Intervention in SEpsis
Acronym
PREVISE
Official Title
Prediction of Severe Sepsis Using a Machine Learning Algorithm
Study Type
Interventional

2. Study Status

Record Verification Date
September 2021
Overall Recruitment Status
Completed
Study Start Date
July 1, 2017 (Actual)
Primary Completion Date
August 30, 2017 (Actual)
Study Completion Date
August 30, 2017 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Dascena
Collaborators
Cabell Huntington Hospital

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No

5. Study Description

Brief Summary
In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Sepsis, Septic Shock, Severe Sepsis

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Factorial Assignment
Masking
None (Open Label)
Allocation
Non-Randomized
Enrollment
2296 (Actual)

8. Arms, Groups, and Interventions

Arm Title
With InSight
Arm Type
Experimental
Arm Description
Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.
Arm Title
Without Insight
Arm Type
Active Comparator
Arm Description
Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.
Intervention Type
Other
Intervention Name(s)
Severe Sepsis Prediction
Intervention Description
Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Intervention Type
Other
Intervention Name(s)
Severe Sepsis Detection
Intervention Description
Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Primary Outcome Measure Information:
Title
In-hospital mortality
Time Frame
Through study completion, an average of 30 days
Secondary Outcome Measure Information:
Title
Hospital length of stay
Time Frame
Through study completion, an average of 30 days
Other Pre-specified Outcome Measures:
Title
Hospital readmission
Time Frame
Through study completion, an average of 30 days
Title
ICU length of stay
Time Frame
Through study completion, an average of 30 days

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible. Exclusion Criteria: All patients younger than 18 years of age will be excluded.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Hoyt Burdick
Organizational Affiliation
Cabell Huntington Hospital
Official's Role
Principal Investigator
Facility Information:
Facility Name
Cabell Huntington Hospital
City
Huntington
State/Province
West Virginia
ZIP/Postal Code
25701
Country
United States

12. IPD Sharing Statement

Citations:
PubMed Identifier
27489621
Citation
Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.
Results Reference
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PubMed Identifier
27208704
Citation
Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.
Results Reference
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PubMed Identifier
27694098
Citation
Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.
Results Reference
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Predictive algoRithm for EValuation and Intervention in SEpsis

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