QJM Advance Access originally published online on September 20, 2005
QJM 2005 98(11):813-820; doi:10.1093/qjmed/hci120
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A simple prediction algorithm for bacteraemia in patients with acute febrile illness
From the 1Department of Medicine, Okinawa Chubu Hospital, Okinawa, Japan, and 2Department of Medicine, University of Florida, Gainesville, Florida, and University of Hawaii, Honolulu, Hawaii, USA
Address correspondence to Dr Y. Tokuda, 281 Miyasato, Uruma city, Okinawa, 904-2293 Japan. email: tokuyasu{at}orange.ocn.ne.jp
Received 24 April 2005 and in revised form 6 August 2005
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.