An Algorithm Driven Sepsis Prediction Biomarker

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ClinicalTrials.gov Identifier: NCT03015454

Recruitment Status : Completed

First Posted : January 10, 2017

Last Update Posted : September 23, 2021

View this study on Beta.ClinicalTrials.gov

Sponsor:

Collaborator:

University of California, San Francisco

Information provided by (Responsible Party):

Dascena

Study Description

Brief Summary:

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).

Condition or disease	Intervention/treatment	Phase
Sepsis Septic Shock Severe Sepsis	Other: Severe Sepsis Prediction Other: Severe Sepsis Detection	Not Applicable

Study Design

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Study Type :	Interventional (Clinical Trial)
Actual Enrollment :	142 participants
Allocation:	Randomized
Intervention Model:	Factorial Assignment
Masking:	None (Open Label)
Primary Purpose:	Screening
Official Title:	A Randomized Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker
Study Start Date :	December 2016
Actual Primary Completion Date :	February 2017

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Sepsis

U.S. FDA Resources

Arms and Interventions

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.	Other: Severe Sepsis Prediction Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly. Other: Severe Sepsis Detection 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.
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.	Other: Severe Sepsis Detection 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.

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.

Other: Severe Sepsis Prediction

Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.

Other: Severe Sepsis Detection

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.

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.

Other: Severe Sepsis Detection

Outcome Measures

Primary Outcome Measures :

Hospital length of stay [ Time Frame: Through study completion, an average of 45 days ]

Secondary Outcome Measures :

In-hospital mortality [ Time Frame: Through study completion, an average of 45 days ]

Other Outcome Measures:

ICU length of stay [ Time Frame: Through study completion, an average of 45 days ]

Eligibility Criteria

Information from the National Library of Medicine

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

Criteria

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.

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): NCT03015454

Locations

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United States, California
UCSF Moffit-Long Hospital
San Francisco, California, United States, 94143

Sponsors and Collaborators

Dascena

University of California, San Francisco

Investigators

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Principal Investigator:	Ritankar Das	Dascena

More Information

Publications:

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.

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.

Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):

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.

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Responsible Party:	Dascena
ClinicalTrials.gov Identifier:	NCT03015454
Other Study ID Numbers:	16-19647
First Posted:	January 10, 2017 Key Record Dates
Last Update Posted:	September 23, 2021
Last Verified:	September 2021

Keywords provided by Dascena:


InSight Dascena

Additional relevant MeSH terms:

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Sepsis Toxemia Infections	Systemic Inflammatory Response Syndrome Inflammation Pathologic Processes

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