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Purpose

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

Eligibility

Eligible Ages
Over 18 Years
Eligible Sex
All
Accepts Healthy Volunteers
No

Inclusion Criteria

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

Exclusion Criteria

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

Study Design

Phase
N/A
Study Type
Interventional
Allocation
Non-Randomized
Intervention Model
Parallel Assignment
Intervention 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).
Primary Purpose
Prevention
Masking
Triple (Participant, Care Provider, Outcomes Assessor)
Masking Description
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.

Arm Groups

ArmDescriptionAssigned Intervention
Experimental
Intervention Arm 		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
    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.
Active Comparator
Control Arm 		Control Arm (active comparator): hospital sites that do not implement eCARTv5 will be active comparator.
  • 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.

Recruiting Locations

More Details

NCT ID
NCT05893420
Status
Active, not recruiting
Sponsor
AgileMD, Inc.

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.

Notice

Study information shown on this site is derived from ClinicalTrials.gov (a public registry operated by the National Institutes of Health). The listing of studies provided is not certain to be all studies for which you might be eligible. Furthermore, study eligibility requirements can be difficult to understand and may change over time, so it is wise to speak with your medical care provider and individual research study teams when making decisions related to participation.