Inconsistency of a model aimed at predicting bacteremia in hospitalized patients

J Clin Epidemiol. 1993 Sep;46(9):1035-40. doi: 10.1016/0895-4356(93)90171-v.


Clinical prediction rules can help physicians determine the necessity for blood cultures in specific patients and/or in whom empiric antibiotic treatment should be administered. Before adopting a prediction rule its validity must be evaluated in different settings. We revealed independent predictors of true bacteremia and developed a risk score based on them in one group of adult hospitalized patients (n = 474; derivation set). An attempt was made to validate this risk score in a second group of in-patients at the same hospital (n = 438; validation set). The derivation set included 540 blood culture episodes and the validation set 516. A blood culture episode was defined as one or more of all blood specimens withdrawn for culture from one patient over one 24 hour period. Independent multivariate predictors of true bacteremia were: temperature of 39 degrees C or higher, current immunosuppressive therapy, serum alkaline phosphatase > 100 IU and hospitalization in an intensive care unit. In the low risk group, defined by the absence of the said predictors, the rates of true bacteremia were 5.1 and 4.6% for the derivation and validation sets, respectively. As raised temperature is the main clinical feature guiding physicians to suspect bacteremia, we examined the probability of true bacteremia in patients with a temperature of less than 38 degrees C and found it to be 5.6% in the two sets. The model identified high risk subset patient groups demonstrating true bacteremia in 38% of all episodes in the derivation set and the comparatively low rate of 12.1% (p < 0.01) for the validation set.(ABSTRACT TRUNCATED AT 250 WORDS)

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Age Distribution
  • Aged
  • Bacteremia / epidemiology*
  • Bacteremia / microbiology
  • Bacteria / isolation & purification
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Israel / epidemiology
  • Logistic Models
  • Male
  • Middle Aged
  • Models, Biological*
  • Multivariate Analysis
  • Odds Ratio
  • Prognosis
  • Prospective Studies
  • Reproducibility of Results
  • Risk Factors
  • Sex Distribution