Clinical trial NCT05335135: Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
Official title: Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock
Lead sponsor: Stocastic, LLC
Sponsor class: INDUSTRY
Collaborators: name: University of Kansas
name: Beckman Coulter, Inc.
name: Truman Medical Center
First posted: 2022-04-19
Enrollment: 300000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All emergency department visits by adult patients during the study period will be included in our analysis.

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.


Eligibility criteria: Inclusion Criteria: Adult patients receiving care at a study site ED

Exclusion Criteria: None

Primary outcomes:
measure: Critical Care
timeFrame: baseline (pre-intervention)
measure: Critical Care
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: In-Hospital Mortality
timeFrame: baseline (pre-intervention)
measure: In-Hospital Mortality
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Emergent Surgery
timeFrame: baseline (pre-intervention)
measure: Emergent Surgery
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Sepsis
timeFrame: baseline (pre-intervention)
measure: Sepsis
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Septic Shock
timeFrame: baseline (pre-intervention)
measure: Septic Shock
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Viral Infection
timeFrame: baseline (pre-intervention)
measure: Viral Infection
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
Secondary outcomes:
Other outcomes:
measure: Critical care triage capture rate
timeFrame: baseline (pre-intervention)
measure: Critical care triage capture rate
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Hospital admission triage capture rate
timeFrame: baseline (pre-intervention)
measure: Hospital admission triage capture rate
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: ED patient flow metrics
timeFrame: baseline (pre-intervention)
measure: ED patient flow metrics
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
measure: Sepsis care quality metrics
timeFrame: baseline (pre-intervention)
measure: Sepsis care quality metrics
timeFrame: during post-implementation steady state (approximately 3 months after intervention)
Locations:
city: Kansas City
country: United States
facility: Kansas University Medical Center
state: Kansas
city: Kansas City
country: United States
facility: University Health Truman Medical Center
state: Missouri
Study officials:
affiliation: Stocastic, LLC
name: Scott Levin, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Stocastic, LLC
name: Jeremiah Hinson, PhD/MD
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Kansas
name: Nima Sarani, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Truman Medical Center
name: Kevin O'Rourke, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.