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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Article info
Publication history
Published online: February 14, 2018
Accepted:
December 18,
2017
Received in revised form:
December 6,
2017
Received:
February 4,
2017
Identification
Copyright
© 2018 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
