ABSTRACT
Rationale Early
detection of
clinical deterioration using
early warning scores may improve outcomes. However, most implemented scores were developed using
logistic regression, only underwent retrospective internal validation, and were not tested in important
patient subgroups.
Objectives:
To develop a gradient boosted machine model (eCARTv5) for identifying
clinical deterioration and then validate externally, test prospectively, and evaluate across
patient subgroups.
Methods:
All
adult patients hospitalized on the wards in seven
hospitals from 2008- 2022 were used to develop eCARTv5, with demographics,
vital signs, clinician
documentation, and
laboratory values utilized to predict
intensive care unit transfer or
death in the next 24 hours. The model was externally validated retrospectively in 21
hospitals from 2009-2023 and prospectively in 10
hospitals from February to May 2023. eCARTv5 was compared to the Modified
Early Warning Score (MEWS) and the National
Early Warning Score (NEWS) using the area under the
receiver operating characteristic curve (AUROC). Measurements and Main
Results:
The development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 46,330 admissions. In retrospective validation, eCART had the highest AUROC (0.835; 95%CI 0.834, 0.835), followed by NEWS (0.766 (95%CI 0.766, 0.767)), and MEWS (0.704 (95%CI 0.703, 0.704)). eCART's performance remained high (AUROC ≥0.80) across a range of
patient demographics, clinical conditions, and during prospective validation.
Conclusions:
We developed eCARTv5, which accurately identifies early
clinical deterioration in hospitalized ward
patients. Our model performed better than the NEWS and MEWS retrospectively, prospectively, and across a range of subgroups.