NCT03015454

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).

Trial Health

80
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
142

participants targeted

Target at P50-P75 for not_applicable sepsis

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

December 1, 2016

Completed
1 month until next milestone

First Submitted

Initial submission to the registry

December 31, 2016

Completed
10 days until next milestone

First Posted

Study publicly available on registry

January 10, 2017

Completed
22 days until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 1, 2017

Completed
Last Updated

September 23, 2021

Status Verified

September 1, 2021

Enrollment Period

2 months

First QC Date

December 31, 2016

Last Update Submit

September 17, 2021

Conditions

SepsisSeptic ShockSevere Sepsis

Keywords

InSightDascena

Outcome Measures

Primary Outcomes (1)

  • Hospital length of stay

    Through study completion, an average of 45 days

Secondary Outcomes (1)

  • In-hospital mortality

    Through study completion, an average of 45 days

Other Outcomes (1)

  • ICU length of stay

    Through study completion, an average of 45 days

Study Arms (2)

With InSight

EXPERIMENTAL

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 PredictionOther: Severe Sepsis Detection

Without InSight

ACTIVE COMPARATOR

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

Interventions

Severe Sepsis PredictionOTHER

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

With InSight
Severe Sepsis DetectionOTHER

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.

With InSightWithout InSight

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • All adult patients admitted to the participating units will be eligible.

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

UCSF Moffit-Long Hospital

San Francisco, California, 94143, United States

Location

Related Publications (5)

  • 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.

    PMID: 27026611BACKGROUND

  • 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.

    PMID: 27489621BACKGROUND

  • 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.

    PMID: 27208704BACKGROUND

  • 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.

    PMID: 27694098BACKGROUND

  • 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.

    PMID: 29435343DERIVED

MeSH Terms

Conditions

SepsisShock, Septic

Condition Hierarchy (Ancestors)

InfectionsSystemic Inflammatory Response SyndromeInflammationPathologic ProcessesPathological Conditions, Signs and SymptomsShock

Study Officials

  • Ritankar Das

    Dascena

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
SCREENING
Intervention Model
FACTORIAL
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 31, 2016

First Posted

January 10, 2017

Study Start

December 1, 2016

Primary Completion

February 1, 2017

Last Updated

September 23, 2021

Record last verified: 2021-09

Locations

US(1)