NCT03644940

Brief Summary

The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a subpopulation-optimized algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe 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. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.

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 Dec 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withdrawn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

August 16, 2018

Completed
7 days until next milestone

First Posted

Study publicly available on registry

August 23, 2018

Completed
2.3 years until next milestone

Study Start

First participant enrolled

December 1, 2020

Completed
7 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 1, 2021

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

July 1, 2021

Completed
Last Updated

September 23, 2021

Status Verified

September 1, 2021

Enrollment Period

7 months

First QC Date

August 16, 2018

Last Update Submit

September 17, 2021

Conditions

SepsisSevere SepsisSeptic Shock

Keywords

Dascenapatient mortalitymachine learningalgorithmdiagnostic

Outcome Measures

Primary Outcomes (1)

  • In-hospital SIRS-based mortality

    Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay

    Through study completion, an average of 8 months

Secondary Outcomes (3)

  • In-hospital severe sepsis/shock-coded mortality

    Through study completion, an average of 8 months

  • SIRS-based hospital length of stay

    Through study completion, an average of 8 months

  • Severe sepsis/shock-coded hospital length of stay

    Through study completion, an average of 8 months

Study Arms (2)

Subpopulation-specific Algorithm

EXPERIMENTAL
Diagnostic Test: CustomSight

Control Algorithm

NO INTERVENTION

Interventions

CustomSightDIAGNOSTIC_TEST

Subpopulation-specific clinical decision support (CDS) system for severe sepsis detection

Subpopulation-specific Algorithm

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
All adults above age 18 who are a member of one of the eight subpopulations studied in this trial (Cardiology, Gastroenterology (GI), Intensive Care Unit (ICU), Medicine, Oncology, Surgery, Transplant, and Emergency Department (ED)) are eligible to participate in the study.

Contact the study team to discuss eligibility requirements. They can help determine if this study is right for you.

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

August 16, 2018

First Posted

August 23, 2018

Study Start

December 1, 2020

Primary Completion

July 1, 2021

Study Completion

July 1, 2021

Last Updated

September 23, 2021

Record last verified: 2021-09