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
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
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.
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.
Inclusion Criteria:
Exclusion Criteria: