Volume 5, Issue 1 p. E46-E52
Original Research

Performance of the maximum modified early warning score to predict the need for higher care utilization among admitted emergency department patients

Corey R. Heitz MD

Corey R. Heitz MD

Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

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John P. Gaillard MD

John P. Gaillard MD

Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

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Howard Blumstein MD

Howard Blumstein MD

Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

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Doug Case PhD

Doug Case PhD

Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina

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Catherine Messick MD

Catherine Messick MD

Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

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Chadwick D. Miller MD

Corresponding Author

Chadwick D. Miller MD

Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Telephone: 336-716-1740; Fax: 336-716-1705

Department of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157Search for more papers by this author
First published: 08 January 2010
Citations: 16

This study was supported in part by the Division of Healthcare Research and Quality of Wake Forest University Baptist Medical Center. Financial support for Dr. Miller is provided by a research scholar award from the Wake Forest University Translational Science Institute.

Disclosure: Nothing to report.

Abstract

BACKGROUND:

It is uncertain whether ED-calculated risk scores can predict required intensity of care upon hospital admission. This investigation examines whether versions of the Modified Early Warning Score (MEWS) predict high level of care utilization among patients admitted from the ED.

METHODS:

A retrospective chart review of 299 admissions was implemented. Exclusions prior to abstraction included pediatrics, cardiology, or trauma admissions. Using a data-gathering instrument, abstractors recorded physiologic parameters and clinical variables. Risk scores were calculated electronically. In contrast to the original MEWS, the MEWS Max was calculated using data from the entire ED visit. The primary outcome composite included all-cause mortality and higher care utilization within 24 hours.

RESULTS:

The final analysis contained 280 participants. 76 (27%) met the composite endpoint of death (n = 1) or higher care utilization (n = 76). The MEWS Max was associated with the composite outcome (OR=l.6 [95% CI 1.3-1.8] for each one point increase). The MEWS Max had moderate predictive ability (C statistic: MEWS Max 0.73 [0.66-0.79]) but classified 82% of participants as intermediate (10-40%) risk. Inclusion of additional variables slightly improved the predictive ability (C statistic 0.76 [0.69–0.82]) and correctly reclassified 17% of patients as <10% risk.

CONCLUSIONS:

The MEWS Max has moderate ability to predict the need for higher level of care. Addition of ED length of stay and other variables to MEWS Max may identify patients at both low and high risk of requiring a higher level of care. Journal of Hospital Medicine 2010;5:E46–E52. © 2010 Society of Hospital Medicine.