eCARTv5 accurately predicted clinical deterioration within 24 hours with an AUROC of 0.834, outperforming eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across patient subgroups.
Observational (n=2,876,898)
Yes
Does eCARTv5 improve the prediction of ICU transfer or death in the next 24 hours in adult patients admitted to inpatient medical-surgical wards compared to MEWS, NEWS, and eCARTv2?
The eCARTv5 machine-learning early warning score outperformed existing scores (MEWS, NEWS, eCARTv2) in predicting clinical deterioration (ICU transfer or death) in hospitalized ward patients, supporting its FDA clearance.
Effect estimate: AUROC 0.834 (95% CI 0.834-0.835)
Absolute Event Rate: 0.834% vs 0.775%
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups. OBJECTIVE: The objective of our multicenter retrospective and prospective observational study was to develop and prospectively validate a gradient-boosted machine model (eCARTv5) for identifying clinical deterioration on the wards. DERIVATION COHORT: All adult patients admitted to the inpatient medical-surgical wards at seven hospitals in three health systems for model development (2006-2022). VALIDATION COHORT: All adult patients admitted to the inpatient medical-surgical wards and at 21 hospitals from three health systems for retrospective (2009-2023) and prospective (2023-2024) external validation. PREDICTION MODEL: Predictor variables (demographics, vital signs, documentation, and laboratory values) were used in a gradient-boosted trees algorithm to predict ICU transfer or death in the next 24 hours. The developed model (eCARTv5) was compared with the Modified Early Warning Score (MEWS), the National Early Warning Score (NEWS), and eCARTv2 using the area under the receiver operating characteristic curve (AUROC). RESULTS: The development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 205,946 admissions. In retrospective validation, eCARTv5 had the highest AUROC (0.834; 95% CI, 0.834-0.835), followed by eCARTv2 (0.775 95% CI, 0.775-0.776), NEWS (0.766 95% CI, 0.766-0.767), and MEWS (0.704 95% CI, 0.703-0.704). eCARTv5's performance remained high (AUROC ≥0.80) across a range of patient demographics, clinical conditions, and during prospective validation. CONCLUSION: We developed eCARTv5, which performed better than eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across a range of subgroups. These results served as the foundation for Food and Drug Administration clearance for its use in identifying deterioration in hospitalized ward patients.
Churpek et al. (Wed,) conducted a observational in Clinical deterioration in hospitalized ward patients (n=2,876,898). eCARTv5 (gradient-boosted machine-learning early warning score) vs. eCARTv2, National Early Warning Score (NEWS), and Modified Early Warning Score (MEWS) was evaluated on Area under the receiver operating characteristic curve (AUROC) for predicting ICU transfer or death in the next 24 hours (retrospective validation) (AUROC 0.834, 95% CI 0.834-0.835). eCARTv5 accurately predicted clinical deterioration within 24 hours with an AUROC of 0.834, outperforming eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across patient subgroups.