Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic ShockNCT05335135
Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
An observational study of Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock, run by Stocastic, LLC at 2 sites in United States.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock
- What is being tested?
- TriageGO-MDW Clinical Decision Support
- How many people
- 300,000 participants
- Where
- 2 sitesUnited States
- Study dates
- 1 Feb 2022to Jan 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: All emergency department visits by adult patients during the study period will be included in our analysis.
Inclusion Criteria: Adult patients receiving care at a study site ED
Exclusion Criteria: None
Trial locations and contacts
2 sites in 1 country.
United States2 sites, 2 recruiting
- Recruiting
Kansas University Medical Center
Kansas City, Kansas, 66160
Nima Sarani, MD
- Recruiting
University Health Truman Medical Center
Kansas City, Missouri, 64108
Kevin O'Rourke, MD
About this study
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.
Read more
(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.
- Being tested
- TriageGO-MDW Clinical Decision Support, Usual Care
- Medical subject headings
- Sepsis, Shock, Septic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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
Usual CareOther
Clinical care without decision support provided by TriageGo-MDW
| Study group | Role | What they receive |
|---|---|---|
| Pre-Implementation | Usual care will be provided during all ED patient encounters. Receives: 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. Receives: Other: TriageGO-MDW Clinical Decision Support |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
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
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
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
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
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
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
Measured: baseline (pre-intervention)
Sepsis
Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured
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
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
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
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
Measured: during post-implementation steady state (approximately 3 months after intervention)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2022
- Study start1 Feb 2022
- Primary completionJan 2023 (estimated)
- Study completionJan 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic ShockStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 300,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Stocastic, LLC
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- University of Kansas, Beckman Coulter, Inc., Truman Medical Center
- Responsible party
- Sponsor
- Investigators
- Scott Levin, PhD, Principal investigator, Stocastic, LLCJeremiah Hinson, PhD/MD, Principal investigator, Stocastic, LLCNima Sarani, MD, Principal investigator, University of KansasKevin O'Rourke, MD, Principal investigator, Truman Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05335135
- Sponsor's protocol ID
- 21-STOC-101
- Status last verified
- Mar 2022
- First submitted
- 22 Feb 2022
- Last updated
- 19 Apr 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 28888332Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, Hager D, Levin S; New Collective Author. 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.
PubMed 27133736Crouser 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.
PubMed 28625579
Study at a glance
From the registry record, last updated by the sponsor on 19 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Linking Novel Diagnostics With Data-Driven Clinical Decision Support in the Emergency Department
- ClinicalTrials.gov ID
- NCT05335135
- Acronym / sponsor ID
- 21-STOC-101
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300,000 participants (estimated)
- Lead sponsor
- Stocastic, LLCIndustry (pharma, biotech, devices)
- Collaborators
- University of Kansas, Beckman Coulter, Inc., Truman Medical Center
- Study start
- 1 Feb 2022 (actual)
- Primary completion
- Jan 2023 (estimated)
- Study completion
- Jan 2024 (estimated)
- First posted
- 19 Apr 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Pre-Implementation | – | Other: Usual CareUsual care will be provided during all ED patient encounters. |
| Post-Implementation | – | Other: TriageGO-MDW Clinical Decision SupportTriageGo-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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- All emergency department visits by adult patients during the study period will be included in our analysis.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| TriageGO-MDW Clinical Decision SupportTriageGO-MDW is non-device clinical decision support that provides patient-level clinical risk estimates based on clinical data derived from the electronic health record | Other | Post-Implementation |
| Usual CareClinical care without decision support provided by TriageGo-MDW | Other | Pre-Implementation |
Primary outcome
Critical Care
Time frame: baseline (pre-intervention)
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
Critical Care
Time frame: during post-implementation steady state (approximately 3 months after intervention)
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
In-Hospital Mortality
Time frame: baseline (pre-intervention)
Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
In-Hospital Mortality
Time frame: during post-implementation steady state (approximately 3 months after intervention)
Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured
Emergent Surgery
Time frame: baseline (pre-intervention)
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
Emergent Surgery
Time frame: during post-implementation steady state (approximately 3 months after intervention)
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
Sepsis
Time frame: baseline (pre-intervention)
Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured
Sepsis
Time frame: during post-implementation steady state (approximately 3 months after intervention)
Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured
Septic Shock
Time frame: baseline (pre-intervention)
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
Septic Shock
Time frame: during post-implementation steady state (approximately 3 months after intervention)
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
Viral Infection
Time frame: baseline (pre-intervention)
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
Viral Infection
Time frame: during post-implementation steady state (approximately 3 months after intervention)
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Apr 2022.
- Background: PubMed 28888332Levin 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.
- Background: PubMed 27133736Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, Hager D, Levin S; New Collective Author. 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.
- Background: PubMed 28625579Crouser 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.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Scott Levin, PhD principal investigator
Stocastic, LLC
Jeremiah Hinson, PhD/MD principal investigator
Stocastic, LLC
Nima Sarani, MD principal investigator
University of Kansas
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Inpatient Hospitalization, Intensive Care Unit Admission, Inpatient Mortality, Sepsis and Septic Shock, run by Stocastic, LLC at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Apr 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Eric Hamrock (4013420373, eric.hamrock@stocastic.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Critical Care (time frame: baseline (pre-intervention)). The study lists 12 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05335135, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Apr 2022.