NCT05335135

Brief Summary

The primary objective of this study is to validate the use of an electronic clinical decision support (CDS) tool, TriageGO with Monocyte Distribution Width (TriageGO-MDW), in the emergency department (ED). TriageGO-MDW is non-device CDS designed to support emergency clinicians (nurses, physicians and advanced practice providers) in performing risk-based assessment and prioritization of patients during their ED visit. This study will follow an effectiveness-implementation hybrid design via the following three aims (phases), to be executed sequentially: (Aim 1) Validate the TriageGO-MDW algorithm locally using retrospective data at ED study sites. (Aim 2) Deploy TriageGO-MDW integrated with the electronic medical record (EMR) and perform user assessment. (Aim 3) Evaluate TriageGO-MDW in steady state with respect to clinical, process, and perceived utility outcomes.

Trial Health

43
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Enrollment
300,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Feb 2022

Geographic Reach
1 country

2 active sites

Status
unknown

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

February 1, 2022

Completed
21 days until next milestone

First Submitted

Initial submission to the registry

February 22, 2022

Completed
2 months until next milestone

First Posted

Study publicly available on registry

April 19, 2022

Completed
9 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2023

Completed
1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

January 1, 2024

Completed
Last Updated

April 19, 2022

Status Verified

March 1, 2022

Enrollment Period

11 months

First QC Date

February 22, 2022

Last Update Submit

April 11, 2022

Conditions

Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock

Outcome Measures

Primary Outcomes (12)

  • Critical Care

    Admission to an intensive care unit within 24 hours of ED disposition; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • Critical Care

    Admission to an intensive care unit within 24 hours of ED disposition; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

  • In-Hospital Mortality

    Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • In-Hospital Mortality

    Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

  • Emergent Surgery

    procedure in the operating room within 12 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • Emergent Surgery

    procedure in the operating room within 12 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

  • Sepsis

    Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • Sepsis

    Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

  • Septic Shock

    Meeting septic shock criteria within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • Septic Shock

    Meeting septic shock criteria within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

  • Viral Infection

    Testing positive for influenza or Covid-19 (SARS-CoV-2) infection within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    baseline (pre-intervention)

  • Viral Infection

    Testing positive for influenza or Covid-19 (SARS-CoV-2) infection within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured

    during post-implementation steady state (approximately 3 months after intervention)

Other Outcomes (8)

  • Critical care triage capture rate

    baseline (pre-intervention)

  • Critical care triage capture rate

    during post-implementation steady state (approximately 3 months after intervention)

  • Hospital admission triage capture rate

    baseline (pre-intervention)

  • +5 more other outcomes

Study Arms (2)

Pre-Implementation

Usual care will be provided during all ED patient encounters.

Other: Usual Care

Post-Implementation

TriageGo-MDW CDS will be made available during all ED patient encounters at two points in the ED care continuum: (1) shortly after arrival during initial ED triage (First Triage) and (2) after initial laboratory results have been populated within the EHR. General illness severity estimates will be provided to nurses at ED triage in the form of recommended triage acuity scores (CDS for First Triage). General illness severity estimates along with estimated risk for specific outcomes including sepsis and septic shock will be presented to clinicians after laboratory results have populated (CDS for Early Assessment). TriageGO-MDW risk estimates will be generated by machine learning algorithms using routinely available clinical data as predictor inputs. Nurses and clinicians will receive risk estimates within existing EHR workflows, along with brief and rapidly interpretable explanations of the logic driving each risk estimate.

Other: TriageGO-MDW Clinical Decision Support

Interventions

TriageGO-MDW Clinical Decision SupportOTHER

TriageGO-MDW is non-device clinical decision support that provides patient-level clinical risk estimates based on clinical data derived from the electronic health record

Post-Implementation
Usual CareOTHER

Clinical care without decision support provided by TriageGo-MDW

Pre-Implementation

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

All emergency department visits by adult patients during the study period will be included in our analysis.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Kansas University Medical Center

Kansas City, Kansas, 66160, United States

RECRUITING

University Health Truman Medical Center

Kansas City, Missouri, 64108, United States

RECRUITING

Related Publications (3)

  • Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, Dugas A, Linton B, Kirsch T, Kelen G. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med. 2018 May;71(5):565-574.e2. doi: 10.1016/j.annemergmed.2017.08.005. Epub 2017 Sep 6.

    PMID: 28888332BACKGROUND

  • Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, Hager D, Levin S. An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes. J Emerg Med. 2016 Jun;50(6):910-8. doi: 10.1016/j.jemermed.2016.02.026. Epub 2016 Apr 25.

    PMID: 27133736BACKGROUND

  • Crouser ED, Parrillo JE, Seymour C, Angus DC, Bicking K, Tejidor L, Magari R, Careaga D, Williams J, Closser DR, Samoszuk M, Herren L, Robart E, Chaves F. Improved Early Detection of Sepsis in the ED With a Novel Monocyte Distribution Width Biomarker. Chest. 2017 Sep;152(3):518-526. doi: 10.1016/j.chest.2017.05.039. Epub 2017 Jun 15.

    PMID: 28625579BACKGROUND

MeSH Terms

Conditions

SepsisShock, Septic

Condition Hierarchy (Ancestors)

InfectionsSystemic Inflammatory Response SyndromeInflammationPathologic ProcessesPathological Conditions, Signs and SymptomsShock

Study Officials

  • Scott Levin, PhD

    Stocastic, LLC

    PRINCIPAL INVESTIGATOR

  • Jeremiah Hinson, PhD/MD

    Stocastic, LLC

    PRINCIPAL INVESTIGATOR

  • Nima Sarani, MD

    University of Kansas

    PRINCIPAL INVESTIGATOR

  • Kevin O'Rourke, MD

    Truman Medical Center

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Eric Hamrock

CONTACT

4013420373eric.hamrock@stocastic.com

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
OTHER
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 22, 2022

First Posted

April 19, 2022

Study Start

February 1, 2022

Primary Completion

January 1, 2023

Study Completion

January 1, 2024

Last Updated

April 19, 2022

Record last verified: 2022-03

Locations

US(2)