An Algorithm Driven Sepsis Prediction Biomarker

Sepsis Clinical Trial

Official title:

A Randomized Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03015454
• Other study ID #: 16-19647
• Status: Completed
• Phase: N/A
• Start date: December 2016

Study information

• Verified date: September 2021
• Source: Dascena
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 142
• Est. primary completion date: February 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Sepsis
• Septic Shock
• Severe Sepsis
• Toxemia

Intervention

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

Locations

Country	Name	City	State
United States	UCSF Moffit-Long Hospital	San Francisco	California

Sponsors (2)

Lead Sponsor	Collaborator
Dascena	University of California, San Francisco

Country where clinical trial is conducted

United States, 

References & Publications (4)

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. — View Citation

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. — View 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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	ICU length of stay		Through study completion, an average of 45 days
Primary	Hospital length of stay		Through study completion, an average of 45 days
Secondary	In-hospital mortality		Through study completion, an average of 45 days