Clinical paperCentile-based early warning scores derived from statistical distributions of vital signs☆
Introduction
In 1997, Morgan and colleagues described the first early warning score (EWS) system, designed to alert clinicians to deteriorating patients using aggregate weighted scoring of vital signs.1 Many variations of this scheme have since been published.2 Despite evidence that physiological instability precedes critical clinical deterioration,3, 4, 5, 6 EWS systems have not been shown to improve patient outcomes.2, 7, 8
Most EWS systems use weights and cut-off values for vital signs that are derived from expert opinion.2, 9, 10 We argue that clinical conjecture can be improved upon by statistical determination of the ranges of normality in the at-risk population, for each vital sign observation. In this paper we describe the development of a centile-based system derived from such information.
Section snippets
Data collection
A large dataset comprising 64,622 h of vital-sign data, acquired from 863 acutely ill in-hospital patients using bedside monitors, was used to investigate the statistical properties of the four main vital signs: heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO2), and systolic blood pressure (SysBP). The data came from three clinical studies in the United Kingdom (UK) and United States (US) between 2004 and 2008.6, 11, 12, 13
The methodologies of these studies
Results
The patient population characteristics in each study are shown in Table 1.
Discussion
We have presented the ranges of values for the four main vital signs in at-risk hospitalised populations. We suggest that clinicians would want to be alerted when their patient strays outside the limits of normality defined by these ranges. Conversely, it seems reasonable that patients whose vital signs remain within the limits of normality should not score in an alerting system. Our EWS uses the 10th and 90th centiles to define the no-score limits. A centile-based system provides a statistical
Conclusion
Our centile-based statistical approach is in line with the original objectives of early warning scores, namely the identification of abnormal physiology at the time of the test.1, 22 Observations are treated as being abnormal if they lie at the extremes of the distributions of vital signs acquired from representative sets of at-risk hospitalised patients. EWS systems based on such an approach have the potential to identify patients with abnormal vital signs and are more likely to trigger when
Conflict of interest statement
No conflicts of interest to declare.
Acknowledgements
The work described in this paper was funded by the NIHR Biomedical Research Centre Programme. Dr David Clifton is supported by the Wellcome Trust and EPSRC under grant number WT 088877/Z/09/Z.
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A Spanish translated version of the abstract of this article appears as Appendix in the final online version at doi:10.1016/j.resuscitation.2011.03.006.