SepsisNCT05893420
A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning
A study testing eCARTv5 clinical deterioration monitoring in people with Sepsis and Septicemia, run by AgileMD, Inc. at 3 sites in United States.
Trial summary
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sepsis, Septicemia, Respiratory Failure
- What is being tested?
- eCARTv5 clinical deterioration monitoring
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30,000 participants
- Where
- 3 sitesUnited States
- Study dates
- 31 Dec 2024to 31 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 18 years old
- Admitted to an eCART-monitored medical-surgical unit (scoring location)
Who cannot join
- Younger than 18 years old
- Not admitted to an eCART-monitored medical surgical unit (scoring location)
Trial locations and contacts
3 sites in 1 country.
United States3 sites
BayCare Health System
Clearwater, Florida, 33759
University of Wisconsin Health
Madison, Wisconsin, 53792
Yale New Haven Health System
New Haven, Connecticut, 06510
About this study
Official title: A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Read the detailed description
The objective of this proposal is to rapidly deploy a clinical decision support tool (eCARTv5) within the electronic health record of multiple medical-surgical units. eCART combines a real-time machine learning algorithm for identifying patients at increased risk for intensive care (ICU) transfer and death with clinical pathways to standardize the care of these patients based on a real-time, quantitative assessment of patient risk.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Background:
Clinical deterioration occurs in approximately 5% of hospitalized adults. Delays in recognition of deterioration heighten the risk of adverse outcomes. Machine learning algorithms enhance clinical decision-making and can improve the quality of patient care. However, their impact on clinical outcomes depends not only on the sensitivity and specificity of the algorithm but also on how well that algorithm is integrated into provider workflows and facilitates timely and appropriate intervention.
Preliminary Data:
eCART has been built upon more than a decade of ongoing scientific research and chronicled in numerous peer-reviewed publications. eCART was developed at the University of Chicago by Drs. Dana Edelson and Matthew Churpek. The first version (eCARTv1) was derived and validated using linear logistic regression in a dataset of nearly 60,000 adult ward patients from a single medical center. That model had 16 variables in it and was subsequently validated in silent mode, demonstrating that eCART could alert clinicians more than 24 hours in advance of ICU transfer or cardiac arrest. eCARTv2, derived and validated in a dataset of nearly 270,000 patients from 5 hospitals, improved upon the earlier version by utilizing a cubic spline logistic regression model with 27 variables and demonstrated improved accuracy over the Modified Early Warning Score (MEWS), a commonly used score that can be hand- calculated by nurses at the bedside (AUC 0.77 vs. 0.70 for cardiac arrest, ICU transfer or death). In a multicenter clinical implementation study, eCARTv2 was associated with a 29% relative risk reduction for mortality. In further development of eCART, the University of Chicago research team demonstrated that upgrading from a cubic spline model to a machine learning model, such as a random forest or gradient boosted machine (GBM), could increase the AUC. In the most recent development - eCART v5 - the research team has advanced the analytic using a gradient boosted machine learning model trained on a multi-center dataset of more than 800,000 patient records. Now with 97 variables, this more sophisticated model increases the accuracy by which clinicians can predict clinical deterioration.
- Conditions
- Sepsis, Septicemia, Respiratory Failure, Hemodynamic Instability, COVID-19, Cardiac Arrest
- Being tested
- eCARTv5 clinical deterioration monitoring, Standard of care control
- Keywords
- machine learning, artificial intelligence, early warning scores, clinical decision support
- Medical subject headings
- Sepsis, Respiratory Insufficiency, COVID-19, Heart Arrest, Clinical Deterioration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
eCARTv5 clinical deterioration monitoringDevice
eCART is a predictive analytic used for the identification of acute clinical deterioration built upon more than a decade of ongoing scientific research and chronicled in numerous peer-reviewed publications. eCART draws upon readily available patient data from the EHR, rapidly quantifies disease severity, and predicts the likelihood of critical illness onset.
Standard of care controlOther
Standard of care is the health system's clinical best practices and workflows for identifying high-risk patients for clinical deterioration, including other tools already built into the electronic health record (EHR). Hospitals that do not implement eCARTv5 will be compared as a control against hospitals that do implement eCARTv5.
| Study group | Role | What they receive |
|---|---|---|
| Intervention Arm | Experimental | Intervention Arm (experimental): eCARTv5 will monitor all adult medical-surgical (ward) patients at hospitals that implement the tool in their EHR. A pre vs. post analysis will be done to compare the impact of the tool at the intervention hospitals. Receives: Device: eCARTv5 clinical deterioration monitoring |
| Control Arm | Active comparator | Control Arm (active comparator): hospital sites that do not implement eCARTv5 will be active comparator. Receives: Other: Standard of care control |
What is being measured
The primary outcome is the main question the study is designed to answer.
Hospital mortality for elevated risk patients
Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient. This data will be taken from the complete hospitalization, from admission to discharge.
Measured: The outcome of hospital mortality for elevated risk patients will be tracked across 12 months
Secondary outcomes (3)
Total hospital length of stay (LOS) for elevated risk patients
Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization, defined as the time period between hospital admission and discharge. LOS is defined as the time (hours or fraction of a day) from first vital sign to last vital sign within a patient encounter.
Measured: Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months
ICU-free days following an eCART elevation
30-day ICU-free days, defined as the number of days patients were both alive and not being cared for in an ICU in the first 30 days following hospital admission with any elevated eCART score. Because death is biased toward fewer ICU days and is a competing outcome, patients who die prior to day 30 are assigned with 0 ICU-free days.
Measured: The outcome of 30-day ICU-free days will be tracked across 12 months
Ventilator-free days following an eCART elevation
30-day ventilator-free days, defined as the number of days patients were both alive and not mechanically ventilated in the first 30 days following hospital admission with any elevated eCART score. Because death is biased toward fewer ventilator days and is a competing outcome, patients who die prior to day 30 are assigned with 0 ventilator-free days.
Measured: The outcome of 30-day ventilator-free days will be tracked across 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jun 2023
- Study start31 Dec 2024
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for SepsisStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- This a parallel study with an intervention group of medical-surgical patients where the tool will be used by providers, and a control group wherein the tool will run silently in the background. The primary analysis will utilize a delta-delta design comparing the intervention hospitals' pre vs. post results to the control hospitals' pre vs. post results. The primary analysis will be limited to patients who ever had an elevated eCARTv5 as those are the ones who would have been eligible for intervention (viewing of the eCARTv5 trend and following the clinical pathway).
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Outcomes assessor
- Enrolment
- 30,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- AgileMD, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Biomedical Advanced Research and Development Authority, University of Chicago, BayCare Health System, University of Wisconsin, Madison, Yale University
- Responsible party
- Sponsor
- Investigators
- Dana P Edelson, MD, MS, Study chair, AgileMD, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05893420
- Sponsor's protocol ID
- 1.0
- Status last verified
- Jul 2025
- First submitted
- 24 Apr 2023
- Last updated
- 29 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs*. Crit Care Med. 2012 Jul;40(7):2102-8. doi: 10.1097/CCM.0b013e318250aa5a.
PubMed 22584764Churpek MM, Yuen TC, Winslow C, Robicsek AA, Meltzer DO, Gibbons RD, Edelson DP. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55. doi: 10.1164/rccm.201406-1022OC.
PubMed 25089847Kang MA, Churpek MM, Zadravecz FJ, Adhikari R, Twu NM, Edelson DP. Real-Time Risk Prediction on the Wards: A Feasibility Study. Crit Care Med. 2016 Aug;44(8):1468-73. doi: 10.1097/CCM.0000000000001716.
PubMed 27075140Winslow CJ, Edelson DP, Churpek MM, Taneja M, Shah NS, Datta A, Wang CH, Ravichandran U, McNulty P, Kharasch M, Halasyamani LK. The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial. Crit Care Med. 2022 Sep 1;50(9):1339-1347. doi: 10.1097/CCM.0000000000005492. Epub 2022 Aug 15.
PubMed 35452010
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Rapid Diagnostic of Risk in Hospitalized Patients With COVID-19, Sepsis, and Other High-Risk Conditions to Improve Outcomes and Critical Resource Allocation Using Machine Learning
- ClinicalTrials.gov ID
- NCT05893420
- Acronym / sponsor ID
- 1.0
- Conditions
- Sepsis, Septicemia, Respiratory Failure, Hemodynamic Instability, COVID-19, Cardiac Arrest
- Keywords
- machine learning, artificial intelligence, early warning scores, clinical decision support
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 30,000 participants (estimated)
- Lead sponsor
- AgileMD, Inc.Industry (pharma, biotech, devices)
- Collaborators
- Biomedical Advanced Research and Development Authority, University of Chicago, BayCare Health System, University of Wisconsin, Madison, Yale University
- Study start
- 31 Dec 2024 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 7 Jun 2023 (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.
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Detailed description
The objective of this proposal is to rapidly deploy a clinical decision support tool (eCARTv5) within the electronic health record of multiple medical-surgical units. eCART combines a real-time machine learning algorithm for identifying patients at increased risk for intensive care (ICU) transfer and death with clinical pathways to standardize the care of these patients based on a real-time, quantitative assessment of patient risk.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
Background:
Clinical deterioration occurs in approximately 5% of hospitalized adults. Delays in recognition of deterioration heighten the risk of adverse outcomes. Machine learning algorithms enhance clinical decision-making and can improve the quality of patient care. However, their impact on clinical outcomes depends not only on the sensitivity and specificity of the algorithm but also on how well that algorithm is integrated into provider workflows and facilitates timely and appropriate intervention.
Preliminary Data:
eCART has been built upon more than a decade of ongoing scientific research and chronicled in numerous peer-reviewed publications. eCART was developed at the University of Chicago by Drs. Dana Edelson and Matthew Churpek. The first version (eCARTv1) was derived and validated using linear logistic regression in a dataset of nearly 60,000 adult ward patients from a single medical center. That model had 16 variables in it and was subsequently validated in silent mode, demonstrating that eCART could alert clinicians more than 24 hours in advance of ICU transfer or cardiac arrest. eCARTv2, derived and validated in a dataset of nearly 270,000 patients from 5 hospitals, improved upon the earlier version by utilizing a cubic spline logistic regression model with 27 variables and demonstrated improved accuracy over the Modified Early Warning Score (MEWS), a commonly used score that can be hand- calculated by nurses at the bedside (AUC 0.77 vs. 0.70 for cardiac arrest, ICU transfer or death). In a multicenter clinical implementation study, eCARTv2 was associated with a 29% relative risk reduction for mortality. In further development of eCART, the University of Chicago research team demonstrated that upgrading from a cubic spline model to a machine learning model, such as a random forest or gradient boosted machine (GBM), could increase the AUC. In the most recent development - eCART v5 - the research team has advanced the analytic using a gradient boosted machine learning model trained on a multi-center dataset of more than 800,000 patient records. Now with 97 variables, this more sophisticated model increases the accuracy by which clinicians can predict clinical deterioration.
Study design
- Primary purpose
- Prevention
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, outcomes assessor)
- Number of arms
- 2
- Model description
- This a parallel study with an intervention group of medical-surgical patients where the tool will be used by providers, and a control group wherein the tool will run silently in the background. The primary analysis will utilize a delta-delta design comparing the intervention hospitals' pre vs. post results to the control hospitals' pre vs. post results. The primary analysis will be limited to patients who ever had an elevated eCARTv5 as those are the ones who would have been eligible for intervention (viewing of the eCARTv5 trend and following the clinical pathway).
| Arm | Type | Receives |
|---|---|---|
| Intervention Arm | Experimental | Device: eCARTv5 clinical deterioration monitoringIntervention Arm (experimental): eCARTv5 will monitor all adult medical-surgical (ward) patients at hospitals that implement the tool in their EHR. A pre vs. post analysis will be done to compare the impact of the tool at the intervention hospitals. |
| Control Arm | Active comparator | Other: Standard of care controlControl Arm (active comparator): hospital sites that do not implement eCARTv5 will be active comparator. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 18 years old
- Admitted to an eCART-monitored medical-surgical unit (scoring location)
Key exclusion criteria (2 listed)
- Younger than 18 years old
- Not admitted to an eCART-monitored medical surgical unit (scoring location)
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 |
|---|---|---|
| eCARTv5 clinical deterioration monitoringeCART is a predictive analytic used for the identification of acute clinical deterioration built upon more than a decade of ongoing scientific research and chronicled in numerous peer-reviewed publications. eCART draws upon readily available patient data from the EHR, rapidly quantifies disease severity, and predicts the likelihood of critical illness onset. | Device | Intervention Arm |
| Standard of care controlStandard of care is the health system's clinical best practices and workflows for identifying high-risk patients for clinical deterioration, including other tools already built into the electronic health record (EHR). Hospitals that do not implement eCARTv5 will be compared as a control against hospitals that do implement eCARTv5. | Other | Control Arm |
Primary outcome
Hospital mortality for elevated risk patients
Time frame: The outcome of hospital mortality for elevated risk patients will be tracked across 12 months
Hospital mortality, a measure of how many patients died in the hospital, will come from administrative data, specifically from the discharge disposition of each eCART elevated risk patient. This data will be taken from the complete hospitalization, from admission to discharge.
3 secondary outcomes
Total hospital length of stay (LOS) for elevated risk patients
Time frame: Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months
ICU-free days following an eCART elevation
Time frame: The outcome of 30-day ICU-free days will be tracked across 12 months
Ventilator-free days following an eCART elevation
Time frame: The outcome of 30-day ventilator-free days will be tracked across 12 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2025.
- Background: PubMed 22584764Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs*. Crit Care Med. 2012 Jul;40(7):2102-8. doi: 10.1097/CCM.0b013e318250aa5a.
- Background: PubMed 25089847Churpek MM, Yuen TC, Winslow C, Robicsek AA, Meltzer DO, Gibbons RD, Edelson DP. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55. doi: 10.1164/rccm.201406-1022OC.
- Background: PubMed 27075140Kang MA, Churpek MM, Zadravecz FJ, Adhikari R, Twu NM, Edelson DP. Real-Time Risk Prediction on the Wards: A Feasibility Study. Crit Care Med. 2016 Aug;44(8):1468-73. doi: 10.1097/CCM.0000000000001716.
- Background: PubMed 35452010Winslow CJ, Edelson DP, Churpek MM, Taneja M, Shah NS, Datta A, Wang CH, Ravichandran U, McNulty P, Kharasch M, Halasyamani LK. The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial. Crit Care Med. 2022 Sep 1;50(9):1339-1347. doi: 10.1097/CCM.0000000000005492. Epub 2022 Aug 15.
- Individual participant dataSharing planned: No
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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.
Dana P Edelson, MD, MS study chair
AgileMD, Inc.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing eCARTv5 clinical deterioration monitoring in people with Sepsis and Septicemia, run by AgileMD, Inc. at 3 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?
The sponsor lists 2 inclusion and 2 exclusion criteria. 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: under way, not looking for participants, as of the sponsor's last update on 29 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Hospital mortality for elevated risk patients (time frame: The outcome of hospital mortality for elevated risk patients will be tracked across 12 months). 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 NCT05893420, which also lists 4 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 29 Jul 2025; AzTrials last refreshed its copy on 7 Nov 2026.