Clinical trial NCT05893420: A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning
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
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Sepsis
Septicemia
Respiratory Failure
Hemodynamic Instability
COVID-19
Cardiac Arrest
Clinical Deterioration
Keywords: machine learning
artificial intelligence
early warning scores
clinical decision support
Lead sponsor: AgileMD, Inc.
Sponsor class: INDUSTRY
Collaborators: name: Department of Health and Human Services
name: University of Chicago
name: BayCare Health System
name: University of Wisconsin, Madison
First posted: 2023-06-07
Enrollment: 30000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
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

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.

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.

Eligibility criteria: 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)

Primary outcomes:
measure: Hospital mortality for elevated risk patients
timeFrame: The outcome of hospital mortality for elevated risk patients will be tracked across 12 months
Secondary outcomes:
measure: Total hospital length of stay (LOS) for elevated risk patients
timeFrame: Total hospital length of stay (LOS) for elevated risk patients will be tracked across 12 months
measure: ICU-free days following an eCART elevation
timeFrame: The outcome of 30-day ICU-free days will be tracked across 12 months
measure: Ventilator-free days following an eCART elevation
timeFrame: The outcome of 30-day ventilator-free days will be tracked across 12 months
Other outcomes:
measure: Sepsis Mortality
timeFrame: The outcome of sepsis mortality will be tracked across 12 months
measure: Sepsis Length of Stay (LOS)
timeFrame: The outcome of sepsis length of stay (LOS) will be tracked across 12 months
measure: COVID-19 Mortality
timeFrame: The outcome of COVID-19 mortality will be tracked across 12 months
measure: COVID-19 Length of Stay (LOS)
timeFrame: The outcomes of COVID-19 length of stay (LOS) will be tracked across 12 months
Locations:
city: Clearwater
country: United States
facility: BayCare Health System
state: Florida
city: Madison
country: United States
facility: University of Wisconsin Health
state: Wisconsin
Study officials:
affiliation: AgileMD, Inc.
name: Dana P Edelson, MD, MS
role: STUDY_CHAIR
References:
citation: 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.
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.
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.
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.