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. 2012 Nov;105(11):1083-8.
doi: 10.1093/qjmed/hcs138. Epub 2012 Aug 1.

Effectiveness of Modified Early Warning Score in predicting outcomes in oncology patients

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Effectiveness of Modified Early Warning Score in predicting outcomes in oncology patients

Tim Cooksley et al. QJM. 2012 Nov.

Abstract

Background: Patients at risk of rapid deterioration and critical illness often have preceding changes in physiological parameters. Track and trigger systems, such as the Modified Early Warning Score (MEWS) used in the UK, have been demonstrated to have some utility in identifying these patients particularly among general medical and surgical patients.

Aim: Assess the effectiveness of MEWS and the proposed (NHS Early Warning Score) in oncology patients. Identify the key physiological parameters that predict outcome in this cohort.

Design: We performed a retrospective analysis at a specialist oncology hospital in the North West of England.

Method: The data for 840 patients reviewed by the Outreach Team between April 2009 and January 2011 was analysed. The effectiveness of the MEWS in predicting Critical Care admission and 30 day mortality was assessed. Statistical analysis to identify the key physiological parameters in predicting these two outcomes was also performed.

Results: The MEWS score was statistically significant in predicting both outcome measures (CCU admission P = 0.037 and 30 day mortality P = 0.004). Respiratory rate (P = 0.0003/P = 0.0001) and temperature (P = 0.033/P ≤ 0.0001) were the key physiological variables in predicting clinical deterioration. Blood pressure (P = 0.999/P = 0.619) and pulse rate (P = 0.446/P = 0.051) did not have statistical significance in predicting either outcome. However, analysis of receiver operator curves showed that MEWS had poor value in predicting both outcomes (0.55 and 0.6, respectively).

Conclusion: The currently used track and trigger systems have poor discriminatory value in identifying Oncological patients at risk of deterioration. An adapted score more focused upon the key predictive physiological parameters in this population needs to be developed to produce a more effective tool.

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