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.