title: A simple prediction algorithm for bacteraemia in patients with acute febrile illness.
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.
  
  
  OBJECTIVE
  To develop a simple classification algorithm predicting the risk of bacteraemia.
  
  
  METHODS
  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).
  
  
  CONCLUSIONS
  This simple and sensitive prediction algorithm may be useful for identifying patients at low risk of bacteraemia. Prospective validation is needed in other settings.
authors.affiliation: Department of Medicine, Okinawa Chubu Hospital, Japan. tokuyasu@orange.ocn.ne.jp
authors.forename: Y
authors.initials: Y
authors.lastname: Tokuda
authors.forename: H
authors.initials: H
authors.lastname: Miyasato
authors.forename: G H
authors.initials: GH
authors.lastname: Stein
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016470:Bacteremia / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012307:Risk Factors; D003294:Seizures, Febrile / Q000150:complications*; D012680:Sensitivity and Specificity