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QJM Advance Access published online on September 20, 2005
QJM, doi:10.1093/qjmed/hci120
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© The Author 2005. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received April 24, 2005
Revised August 6, 2005

Original paper

A simple prediction algorithm for bacteraemia in patients with acute febrile illness

Y. Tokuda 1*, H. Miyasato 1, and G.H. Stein 2

1 From the Department of Medicine, Okinawa Chubu Hospital, Okinawa, Japan
2 From the Department of Medicine, University of Florida, Gainesville, Florida, and University of Hawaii, Honolulu, Hawaii, USA

* To whom correspondence should be addressed.
Y. Tokuda, E-mail: tokuyasu{at}orange.ocn.ne.jp


  Abstract

Background: Existing prediction models for the risk of bacteraemia are complex and difficult to use. Physicians are likely to use a model only if it is simple and sensitive.

Aim: To develop a simple classification algorithm predicting the risk of bacteraemia.

Design: Hospital-based study.

Methods: We enrolled 526 adult consecutive patients with acute febrile illness (40 with bacteraemia) presenting to the emergency department at a community hospital in Okinawa, Japan. Recursive partitioning analysis was used to build the classification algorithm with V-fold cross-validation. We used two clinical scenarios: in the first, laboratory tests were not available; in the second, they were.

Results: The two prediction algorithms generated three different risk groups for bacteraemia. In the first scenario, the important variables were chills, pulse, and physician diagnosis of a low-risk site. The low-risk group from this first algorithm included 68% of the total patients; sensitivity was 87.5% and the misclassification rate was 1.4% (5/358). In the second scenario, the important variables were chills, C-reactive protein, and physician diagnosis of a low-risk site. The low-risk group for the second algorithm included 62% of the total patients; sensitivity was 92.5% and misclassification rate 0.9% (3/328). The algorithms had negative predictive values of 98.6% (first scenario) and 99.1% (second).

Discussion: This simple and sensitive prediction algorithm may be useful for identifying patients at low risk of bacteraemia. Prospective validation is needed in other settings.


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