NCT03235193

Brief Summary

In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).

Trial Health

87
On Track

Trial Health Score

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

Enrollment
2,296

participants targeted

Target at P75+ for not_applicable sepsis

Timeline
Completed

Started Jul 2017

Shorter than P25 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

July 1, 2017

Completed
26 days until next milestone

First Submitted

Initial submission to the registry

July 27, 2017

Completed
5 days until next milestone

First Posted

Study publicly available on registry

August 1, 2017

Completed
29 days until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 30, 2017

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

August 30, 2017

Completed
Last Updated

September 21, 2021

Status Verified

September 1, 2021

Enrollment Period

2 months

First QC Date

July 27, 2017

Last Update Submit

September 17, 2021

Conditions

SepsisSeptic ShockSevere Sepsis

Outcome Measures

Primary Outcomes (1)

  • In-hospital mortality

    Through study completion, an average of 30 days

Secondary Outcomes (1)

  • Hospital length of stay

    Through study completion, an average of 30 days

Other Outcomes (2)

  • Hospital readmission

    Through study completion, an average of 30 days

  • ICU length of stay

    Through study completion, an average of 30 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 CHH 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 CHH 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 CHH 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 visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital 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)

Cabell Huntington Hospital

Huntington, West Virginia, 25701, United States

Location

Related Publications (3)

  • 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

MeSH Terms

Conditions

SepsisShock, Septic

Condition Hierarchy (Ancestors)

InfectionsSystemic Inflammatory Response SyndromeInflammationPathologic ProcessesPathological Conditions, Signs and SymptomsShock

Study Officials

  • Hoyt Burdick

    Cabell Huntington Hospital

    PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

First Submitted

July 27, 2017

First Posted

August 1, 2017

Study Start

July 1, 2017

Primary Completion

August 30, 2017

Study Completion

August 30, 2017

Last Updated

September 21, 2021

Record last verified: 2021-09

Locations

US(1)