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Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting

Retrospective Cohort Study

Bartkowiak, Bartlomiej, PhD*; Snyder, Ashley M., MPH*; Benjamin, Andrew, MD; Schneider, Andrew, MD; Twu, Nicole M., MS*; Churpek, Matthew M., MD, MPH, PhD*; Roggin, Kevin K., MD; Edelson, Dana P., MD, MS*

doi: 10.1097/SLA.0000000000002665

Objective: Assess the accuracy of 3 early warning scores for predicting severe adverse events in postoperative inpatients.

Summary of Background Data: Postoperative clinical deterioration on inpatient hospital services is associated with increased morbidity, mortality, and cost. Early warning scores have been developed to detect inpatient clinical deterioration and trigger rapid response activation, but knowledge regarding the application of early warning scores to postoperative inpatients is limited.

Methods: This was a retrospective cohort study of adult patients hospitalized on the wards after surgical procedures at an urban academic medical center from November, 2008 to January, 2016. The accuracies of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and the electronic cardiac arrest risk triage (eCART) score were compared in predicting severe adverse events (ICU transfer, ward cardiac arrest, or ward death) in the postoperative period using the area under the receiver operating characteristic curve (AUC).

Results: Of the 32,537 patient admissions included in the study, 3.8% (n = 1243) experienced a severe adverse outcome after the procedure. The accuracy for predicting the composite outcome was highest for eCART [AUC 0.79 (95% CI: 0.78–0.81)], followed by NEWS [AUC 0.76 (95% CI: 0.75–0.78)], and MEWS [AUC 0.75 (95% CI: 0.73–0.76)]. Of the individual vital signs and labs, maximum respiratory rate was the most predictive (AUC 0.67) and maximum temperature was an inverse predictor (AUC 0.46).

Conclusion: Early warning scores are predictive of severe adverse events in postoperative patients. eCART is significantly more accurate in this patient population than both NEWS and MEWS.

*Department of Medicine, University of Chicago Medicine, Chicago, IL

Department of Surgery, University of Chicago Medicine, Chicago, IL.

Reprints: Kevin K. Roggin, MD, Department of Surgery, University of Chicago Medicine, 5841 S. Maryland Avenue, MC 4052, Chicago, IL 60637. E-mail:

KKR and DPE contributed equally to this work.

MMC and DPE have a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. MMC is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080). In addition, DPE has received research support from Philips Healthcare (Andover, MA) and from Early Sense (Tel Aviv, Israel). She has ownership interest in Quant HC (Chicago, IL), which is developing products for risk stratification of hospitalized patients.

The authors declare no conflicts of interest.

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