- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03015454
An Algorithm Driven Sepsis Prediction Biomarker
September 17, 2021 updated by: Dascena
A Randomized Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a large, diverse patient cohort.
In this prospective study, the ability of InSight to predict severe sepsis patients is investigated.
Specifically, InSight is compared with a well established severe sepsis detector in the UCSF electronic health record (EHR).
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Study Type
Interventional
Enrollment (Actual)
142
Phase
- Not Applicable
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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California
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San Francisco, California, United States, 94143
- UCSF Moffit-Long Hospital
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Description
Inclusion Criteria:
- All adult patients admitted to the participating units will be eligible.
Exclusion Criteria:
- All patients younger than 18 years of age will be excluded.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Factorial Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: With InSight
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 UCSF electronic health record.
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Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
Upon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
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Active Comparator: Without InSight
Healthcare provider does not receive any alerts from InSight.
Healthcare provider receives information from the severe sepsis detector in the UCSF electronic health record.
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Upon receiving information from the severe sepsis detector in the UCSF electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
---|---|
Hospital length of stay
Time Frame: Through study completion, an average of 45 days
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Through study completion, an average of 45 days
|
Secondary Outcome Measures
Outcome Measure |
Time Frame |
---|---|
In-hospital mortality
Time Frame: Through study completion, an average of 45 days
|
Through study completion, an average of 45 days
|
Other Outcome Measures
Outcome Measure |
Time Frame |
---|---|
ICU length of stay
Time Frame: Through study completion, an average of 45 days
|
Through study completion, an average of 45 days
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Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Investigators
- Principal Investigator: Ritankar Das, Dascena
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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.
- 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.
- Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28.
- Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start
December 1, 2016
Primary Completion (Actual)
February 1, 2017
Study Registration Dates
First Submitted
December 31, 2016
First Submitted That Met QC Criteria
January 6, 2017
First Posted (Estimate)
January 10, 2017
Study Record Updates
Last Update Posted (Actual)
September 23, 2021
Last Update Submitted That Met QC Criteria
September 17, 2021
Last Verified
September 1, 2021
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 16-19647
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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Clinical Trials on Severe Sepsis Prediction
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