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External validation of a decision tree early warning score using only laboratory data: A retrospective review of prospectively collected data

Published:February 14, 2018DOI:https://doi.org/10.1016/j.ejim.2017.12.008

      Highlights

      • Acceptable ability to identify patients at risk of in-hospital mortality
      • Compatible to other EWS
      • Accurate for most major disease categories studied separately

      Abstract

      Introduction

      Early warning scores (EWS) have been developed to identify the degree of illness severity among acutely ill patients. One system, The Laboratory Decision Tree Early Warning Score (LDT-EWS) is wholly laboratory data based. Laboratory data was used in the development of a rare computerized method, developing a decision tree analysis. This article externally validates LDT-EWS, which is obligatory for an EWS before clinical use.

      Method

      We conducted a retrospective review of prospectively collected data based on a time limited sample of all patients admitted through the medical admission unit (MAU) on a Danish secondary hospital. All consecutive adult patients admitted from 2 October 2008 until 19 February 2009, and from 23 February 2010 until 26 May 2010, were included. Validation was made by calculating the discriminatory power as area under the receiver-operating curve (AUROC) and calibration (precision) as Hosmer-Lemeshow Goodness of fit test.

      Results

      A total of 5858 patients were admitted and 4902 included (83.7%). In-hospital mortality in our final dataset (n = 4902) was 3.5%. Discriminatory power (95% CI), identifying in-hospital death was 0.809 (0.777–0.842). Calibration was good with a goodness-of-fit test of X2 = 5.37 (7 degrees of freedom), p = 0.62.

      Conclusion

      LDT-EWS has acceptable ability to identify patients at high risk of dying during hospitalization with good precision. Further studies performing impact analysis are required before this score should be implemented in clinical practice.

      Keywords

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