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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT03015454
Other study ID # 16-19647
Secondary ID
Status Completed
Phase N/A
First received
Last updated
Start date December 2016

Study information

Verified date September 2021
Source Dascena
Contact n/a
Is FDA regulated No
Health authority
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. completion date
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.

Study Design


Related Conditions & MeSH terms


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
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