NCT03882476

Brief Summary

The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a machine-learning algorithm will be applied to EHR data for the detection of sepsis. For patients determined to have a high risk of sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality.

Trial Health

15
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Timeline
Completed

Started Jan 2020

Shorter than P25 for phase_2 sepsis

Status
withdrawn

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

First Submitted

Initial submission to the registry

March 18, 2019

Completed
2 days until next milestone

First Posted

Study publicly available on registry

March 20, 2019

Completed
10 months until next milestone

Study Start

First participant enrolled

January 1, 2020

Completed
1.2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 28, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

February 28, 2021

Completed
Last Updated

September 23, 2021

Status Verified

September 1, 2021

Enrollment Period

1.2 years

First QC Date

March 18, 2019

Last Update Submit

September 17, 2021

Conditions

SepsisSevere SepsisSeptic Shock

Keywords

Dascenapatient mortalitymachine learningalgorithmdiagnostic

Outcome Measures

Primary Outcomes (1)

  • In-hospital SIRS-based mortality

    Rate of mortality attributed to patients meeting two or more SIRS criteria at some point during their stay

    Through study completion, an average of eight months

Study Arms (2)

Experimental

EXPERIMENTAL

The experimental arm will involve patients monitored by InSight.

Diagnostic Test: InSight

Control

NO INTERVENTION

The control arm will have no intervention and will involve patients with the usual standard of care.

Interventions

InSightDIAGNOSTIC_TEST

Clinical decision support (CDS) system for sepsis detection

Experimental

Eligibility Criteria

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

You may qualify if:

  • During the study period, all patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities will automatically be enrolled in the trial, until the enrollment target for the study is met

You may not qualify if:

  • Patients under the age of 18

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (5)

  • Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R. Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. Biomed Inform Insights. 2017 Jun 12;9:1178222617712994. doi: 10.1177/1178222617712994. eCollection 2017.

    PMID: 28638239BACKGROUND

  • Calvert J, Mao Q, Rogers AJ, Barton C, Jay M, Desautels T, Mohamadlou H, Jan J, Das R. A computational approach to mortality prediction of alcohol use disorder inpatients. Comput Biol Med. 2016 Aug 1;75:74-9. doi: 10.1016/j.compbiomed.2016.05.015. Epub 2016 May 24.

    PMID: 27253619BACKGROUND

  • 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, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.

    PMID: 27699003BACKGROUND

  • 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: 29435343BACKGROUND

MeSH Terms

Conditions

SepsisShock, SepticDisease

Condition Hierarchy (Ancestors)

InfectionsSystemic Inflammatory Response SyndromeInflammationPathologic ProcessesPathological Conditions, Signs and SymptomsShock

Study Officials

  • Ritankar Das, MSc

    Dascena

    PRINCIPAL INVESTIGATOR
0

Study Design

Study Type
interventional
Phase
phase 2
Allocation
RANDOMIZED
Masking
TRIPLE
Who Masked
PARTICIPANT, CARE PROVIDER, INVESTIGATOR
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

March 18, 2019

First Posted

March 20, 2019

Study Start

January 1, 2020

Primary Completion

February 28, 2021

Study Completion

February 28, 2021

Last Updated

September 23, 2021

Record last verified: 2021-09