A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

COVID-19 Clinical Trial

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05893420
• Other study ID #: 1.0
• Status: Recruiting
• Phase: N/A
• Start date: December 1, 2022
• Est. completion date: June 30, 2025

Study information

• Verified date: June 2023
• Source: AgileMD, Inc.
• Contact: Dana P Edelson, MD, MS
• Phone: 415-650-0522
• Email: dana[@]agilemd.com
• Is FDA regulated: No
• Study type: Interventional

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 30000
• Est. completion date: June 30, 2025
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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)

Related Conditions & MeSH terms

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

Intervention

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.

Locations

Country	Name	City	State
United States	BayCare Health System	Clearwater	Florida
United States	University of Wisconsin Health	Madison	Wisconsin

Sponsors (5)

Lead Sponsor	Collaborator
AgileMD, Inc.	BayCare Health System, Department of Health and Human Services, University of Chicago, University of Wisconsin, Madison

Country where clinical trial is conducted

United States, 

References & 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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Sepsis Mortality	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 meeting Sep-1 criteria for sepsis.	The outcome of sepsis mortality will be tracked across 12 months
Other	Sepsis Length of Stay (LOS)	Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization that met Sep-1 criteria for sepsis.	The outcome of sepsis length of stay (LOS) will be tracked across 12 months
Other	COVID-19 Mortality	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 with a COVID-19 diagnosis or positive COVID-19 test result.	The outcome of COVID-19 mortality will be tracked across 12 months
Other	COVID-19 Length of Stay (LOS)	Total hospital length of stay (LOS) for patients with any elevated eCART score during hospitalization with a COVID-19 diagnosis or positive COVID-19 test result.	The outcomes of COVID-19 length of stay (LOS) will be tracked across 12 months
Primary	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	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.	Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months
Secondary	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.	The outcome of 30-day ICU-free days will be tracked across 12 months
Secondary	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.	The outcome of 30-day ventilator-free days will be tracked across 12 months