Machine Learning Monitoring for Clinical Deterioration

Not currently recruiting at 2 trial locations

Overseen By Borna Safabakhsh, MS, MBA

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: AgileMD, Inc.

Disqualifiers: Under 18, Not in eCART unit

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a software tool called eCARTv5, which helps hospital staff identify patients who might deteriorate. It analyzes real-time data, such as vital signs and lab results, to predict if someone might require intensive care or face a risk of dying. The goal is to reduce the duration of ventilator use and hospital stays. It suits adults admitted to specific hospital wards using this monitoring system. As an unphased trial, this study allows patients to contribute to innovative healthcare solutions that could enhance patient outcomes.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It seems focused on monitoring rather than changing treatments, so you may not need to stop them.

What prior data suggests that this software-based clinical decision support tool is safe for monitoring clinical deterioration?

Research shows that eCARTv5, a software tool, identifies patients who might deteriorate in the hospital. Studies indicate that eCARTv5 predicts patient decline more accurately than older versions and other similar tools. As a result, it can identify more at-risk patients with fewer errors.

Being a software tool, eCARTv5 does not cause physical side effects like medications do. The main concern is its effectiveness and whether it provides accurate alerts to doctors and nurses. The tool has improved over time, suggesting increased reliability. No reports of harm from using the software itself have emerged, so it is considered safe for use in hospitals. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the eCARTv5 clinical deterioration monitoring tool because it leverages machine learning to predict and prevent clinical deterioration in hospitalized patients. Unlike traditional methods that rely on manual monitoring by healthcare staff, eCARTv5 continuously analyzes patient data from electronic health records (EHRs) to provide real-time alerts. This proactive approach has the potential to improve patient outcomes by identifying issues before they become critical, offering a significant advancement over standard observation techniques.

What evidence suggests that the eCARTv5 clinical deterioration monitoring is effective for predicting clinical deterioration?

Research has shown that eCARTv5, a machine learning tool, can effectively predict when hospital patients might deteriorate. One study demonstrated that eCARTv5 alerted medical staff about serious health issues a median of 5 hours before they occurred, much earlier than traditional methods. Another study compared eCARTv5 to six other early warning systems and found it excelled at identifying patients at risk. This trial will include an intervention arm where eCARTv5 will monitor all adult medical-surgical patients at hospitals implementing the tool, and a control arm at hospitals not using eCARTv5. The tool uses real-time data from hospital records to identify potential problems, allowing doctors to act sooner to prevent worse outcomes. Overall, eCARTv5 has strong potential to reduce the need for ventilators, shorten hospital stays, and lower death rates in high-risk patients. ^{2 3 6 7 8}

Who Is on the Research Team?

Dana P Edelson, MD, MS

Principal Investigator

AgileMD, Inc.

Are You a Good Fit for This Trial?

This trial is for adults over 18 years old who are admitted to specific hospital wards where the eCARTv5 monitoring system is used. It's not open to those under 18 or patients in wards without this technology.

Inclusion Criteria

I am admitted to a unit with eCART monitoring.

Exclusion Criteria

You are not in a hospital unit where patients are monitored using eCART.

I am under 18 years old.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Deployment of eCARTv5 into the EHR workflow to monitor patients and guide clinical teams

12 months

Follow-up

Participants are monitored for outcomes such as mortality, length of stay, and ventilator-free days

12 months

What Are the Treatments Tested in This Trial?

Interventions

• eCARTv5 clinical deterioration monitoring

Trial Overview The study tests a machine learning tool, eCARTv5, integrated into hospitals' electronic health records. It predicts which patients might soon need intensive care by analyzing their vital signs and lab results.

How Is the Trial Designed?

Group I: Intervention Arm Experimental Treatment 1 Intervention

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.

Group II: Control Arm Active Control 1 Intervention

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

eCARTv5 clinical deterioration monitoring is already approved in United States for the following indications:

Approved in United States as eCARTv5 for:

• Clinical deterioration monitoring in hospitalized ward patients

Who Is Running the Clinical Trial?

AgileMD, Inc.

Lead Sponsor

Trials

2

Recruited

60,000+

Department of Health and Human Services

Collaborator

Trials

240

Recruited

944,000+

University of Wisconsin, Madison

Collaborator

Trials

1,249

Recruited

3,255,000+

University of Chicago

Collaborator

Trials

1,086

Recruited

844,000+

BayCare Health System

Collaborator

Trials

5

Recruited

30,700+

Biomedical Advanced Research and Development Authority

Collaborator

Trials

108

Recruited

574,000+

Yale University

Collaborator

Trials

1,963

Recruited

3,046,000+

Published Research Related to This Trial

The HAVEN system, developed using data from 230,415 patient admissions, significantly outperforms existing early warning systems in predicting in-hospital deterioration, achieving a c-statistic of 0.901 compared to scores ranging from 0.700 to 0.863.

HAVEN can identify 42% of patients at risk of cardiac arrest or unplanned ICU admissions up to 48 hours in advance, providing a crucial lead time for intervention, which is much higher than the 22% identified by the next best scoring system.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System. Pimentel, MAF., Redfern, OC., Malycha, J., et al.[2022]

The study aims to implement a 'Just-in-Time Training' program for nurses using the National Early Warning Score 2 (NEWS2) to enhance early recognition of clinical deterioration in hospitalized patients, which is crucial for improving patient outcomes.

By training nurses to identify early signs of clinical deterioration, the study seeks to reduce mortality rates and the need for emergency interventions, highlighting the importance of timely nursing interventions in patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Nursing Training for Early Clinical Deterioration Risk Assessment: Protocol for an Implementation Study. Lourenço, LBA., Meszaros, MJ., Silva, MFN., et al.[2023]

This systematic review aims to identify and evaluate the automated features, technologies, and algorithms used in electronic early warning/track and triage systems (EW/TTS) designed to predict clinical deterioration, highlighting their potential to improve patient safety.

The study will analyze data from various databases without time limitations, ensuring a comprehensive understanding of current EW/TTS capabilities, which could lead to advancements in fully automated systems for preventing clinical deterioration.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms. Rostam Niakan Kalhori, S., Deserno, TM., Haghi, M., et al.[2023]

The Intensive care Warning Index (I-WIN) model, developed using machine learning on data from 488 infants with congenital heart disease, can predict clinical deterioration up to 8 hours in advance with high accuracy (AUC of 0.92 at 4 hours before deterioration).

This model represents a significant shift towards using data-driven approaches for risk prediction in critical care, potentially improving patient outcomes by allowing timely interventions based on routinely collected electronic health record data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records. Ruiz, VM., Goldsmith, MP., Shi, L., et al.[2022]

Machine learning (ML) techniques have been applied in 29 studies to predict patient clinical deterioration in hospitals, showing a wide range of predictive accuracy with area under the curve values from 0.55 to 0.99, depending on the model and input features used.

Despite the promising advancements in using ML for early identification of at-risk patients, further research is needed to assess the practical application and effectiveness of these models in real-world clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review. Jahandideh, S., Ozavci, G., Sahle, BW., et al.[2023]

The MEWS++ machine learning model can predict patient deterioration or death up to six hours in advance, significantly improving early detection compared to traditional methods, which rely on a limited set of variables.

In a study of 96,645 patients, the random forest model outperformed the traditional Modified Early Warning Score (MEWS) with a sensitivity increase of 37% and an AUC-ROC improvement of 14%, demonstrating its potential for timely clinical intervention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model. Kia, A., Timsina, P., Joshi, HN., et al.[2020]

The eCART Lite machine learning tool, developed using data from 556,848 adult hospital admissions, effectively predicts clinical deterioration using just age, heart rate, and respiratory rate, outperforming existing models like MEWS and NEWS.

With an area under the curve (AUC) of 0.79 for ICU transfer prediction and 0.80 for combined outcomes, eCART Lite demonstrates high accuracy, making it a valuable tool for resource-limited inpatient settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Less is more: Detecting clinical deterioration in the hospital with machine learning using only age, heart rate, and respiratory rate. Akel, MA., Carey, KA., Winslow, CJ., et al.[2022]

A systematic review of 18 studies on real-time automated alerts for clinical deterioration in hospitalized patients found that most did not show significant improvements in patient outcomes, such as ICU admissions or in-hospital deaths.

Only four studies reported positive outcomes, and these were the only ones that involved direct clinician engagement, suggesting that involving healthcare providers may be crucial for the effectiveness of such alert systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes. Blythe, R., Parsons, R., White, NM., et al.[2022]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT05893420

A Rapid Diagnostic of Risk in Hospitalized Patients Using ... eCART is a predictive analytic used for the identification of acute clinical deterioration built upon more than a decade of ongoing scientific research and ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11544488/

Early Warning Scores With and Without Artificial Intelligence In this cohort study that compared 6 early warning scores across 362 926 patient encounters, eCARTv5, a machine learning model, identified ...

3. accessdata.fda.gov accessdata.fda.gov/cdrh_docs/pdf23/K233253.pdf

June 21, 2024 AgileMD, Inc. Kelliann Payne Partner Hogan ... The AgileMD eCARTv5 Clinical Deterioration Suite (“eCART”) is a cloud-based software device that is integrated into the electronic health record ...

4. journals.lww.com journals.lww.com/ccejournal/fulltext/2025/04000/multicenter_development_and_prospective_validation.10.aspx

A Gradient-Boosted Machine-Learning Early Warning Score At the high-risk threshold, eCARTv5 alerted a median of 5 (IQR, 0–43) hours in advance of clinical deterioration, significantly earlier than NEWS (3 hr (IQR, 0– ...

5. pubmed.ncbi.nlm.nih.gov pubmed.ncbi.nlm.nih.gov/40138535/

A Gradient-Boosted Machine-Learning Early Warning Score eCARTv5 is a gradient-boosted machine model for identifying clinical deterioration, using a gradient-boosted trees algorithm to predict ICU ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11949291/

A Gradient-Boosted Machine-Learning Early Warning Score We developed eCARTv5, which performed better than eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across a range of subgroups.

7. jamanetwork.com jamanetwork.com/journals/jamanetworkopen/fullarticle/2824885

Early Warning Scores With and Without Artificial Intelligence In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false ...

8. agilemd.com agilemd.com/

AgileMD Learn how AgileMD provides clinical pathways and clinical deterioration early warning software (eCART) that directly integrates into the health system's EHR ...