A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

NCT05893420 | Active Not Recruiting FDA Device

• 1 other identifier: 1.0
• Study Type: interventional
• Target: 30,000
• Locations: 1 country
• Sites: 3

Brief 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. 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.

Conditions

Sepsis; Septicemia; Respiratory Failure; Hemodynamic Instability; COVID-19; Cardiac Arrest; Clinical Deterioration

Keywords

machine learning; artificial intelligence; early warning scores; clinical decision support

Outcome Measures

Primary Outcomes (1)

• 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.

  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 elevated risk patients will be tracked across 12 months

• ICU-free days following an eCART elevation

  The outcome of 30-day ICU-free days will be tracked across 12 months

• Ventilator-free days following an eCART elevation

  The outcome of 30-day ventilator-free days will be tracked across 12 months

Other Outcomes (4)

• Sepsis Mortality

  The outcome of sepsis mortality will be tracked across 12 months

• Sepsis Length of Stay (LOS)

  The outcome of sepsis length of stay (LOS) will be tracked across 12 months

• COVID-19 Mortality

  The outcome of COVID-19 mortality will be tracked across 12 months

Study Arms (2)

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.

Device: eCARTv5 clinical deterioration monitoring

Control Arm

ACTIVE COMPARATOR

Control Arm (active comparator): hospital sites that do not implement eCARTv5 will be active comparator.

Other: Standard of care control

Interventions

eCARTv5 clinical deterioration monitoring DEVICE

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.

Intervention Arm

Standard of care control OTHER

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.

Control Arm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• years old
• Admitted to an eCART-monitored medical-surgical unit (scoring location)

You may not qualify if:

• Younger than 18 years old
• Not admitted to an eCART-monitored medical surgical unit (scoring location)

Sponsors & Collaborators

• AgileMD, Inc.: lead
• Biomedical Advanced Research and Development Authority: collaborator
• University of Chicago: collaborator
• BayCare Health System: collaborator
• University of Wisconsin, Madison: collaborator
• Yale University: collaborator

Study Sites (3)

Yale New Haven Health System

New Haven, Connecticut, 06510, United States

Location

BayCare Health System

Clearwater, Florida, 33759, United States

Location

University of Wisconsin Health

Madison, Wisconsin, 53792, United States

Location

Related Publications (4)

• 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.

  PMID: 22584764 BACKGROUND

• Churpek 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.

  PMID: 25089847 BACKGROUND

• Kang 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.

  PMID: 27075140 BACKGROUND

• Winslow 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.

  PMID: 35452010 BACKGROUND

MeSH Terms

Conditions

Sepsis; Respiratory Insufficiency; COVID-19; Heart Arrest; Clinical Deterioration

Condition Hierarchy (Ancestors)

Infections; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Respiration Disorders; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Heart Diseases; Cardiovascular Diseases; Disease Progression; Disease Attributes

Study Officials

• Dana P Edelson, MD, MS

  AgileMD, Inc.

  STUDY CHAIR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, CARE PROVIDER, OUTCOMES ASSESSOR
• Masking Details: In control hospitals, eCART will be scoring silently in the background and not visible to the care provider or the patient. Because this is administrative data, the outcomes assessor will similarly be blinded to the score. In the intervention hospitals, care providers will be aware of the score and trained to it. Patients may be aware as a result.
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: 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).

Study Record Dates

• First Submitted: April 24, 2023
• First Posted: June 7, 2023
• Study Start: December 31, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 29, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

US (3)