A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

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

Interventions

DEVICE

eCARTv5 clinical deterioration monitoring

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.

OTHER

Standard of care control

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.

Sponsors & Collaborators

• Biomedical Advanced Research and Development Authority

  collaborator FED

• University of Chicago

  collaborator OTHER

• BayCare Health System

  collaborator OTHER

• University of Wisconsin, Madison

  collaborator OTHER

• Yale University

  collaborator OTHER

• AgileMD, Inc.

  lead INDUSTRY

Principal Investigators

• Dana P Edelson, MD, MS · AgileMD, Inc.

Study Design

Allocation

NON_RANDOMIZED

Purpose

PREVENTION

Masking

TRIPLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-12-31

Primary Completion

2026-12-31

Completion

2026-12-31

FDA Device

Yes

Countries

• United States

Study Locations