Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients. (February 2018) - Green, Malcolm; Lander, Harvey; Snyder, Ashley; Hudson, Paul; Churpek, Matthew; Edelson, Dana
Abstract: Introduction: Traditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration. Objective(s): We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score. Design and participants: Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013. Main outcome measures: Cardiac arrest, ICU transfer or death within 24 h of a score Results: Overall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799–0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716–0.720]; 0.698 [0.696–0.700]; 0.663 [0.661–0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of > = 4 and > = 2, NEWS thresholds of > = 5 and > = 2, and eCART thresholds of > = 12 and > = 4, respectively. At those thresholds, eCART caught 22 more adverse events per 10, 000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer. Conclusion(s): An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24 h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital.