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Predicting enteric fever without bacteriological culture results.

I N Ross1, T Abraham

  • 1Department of Medicine, University Hospital, Universiti Sains Malaysia, Kubang Kerian, Kelantan.

Transactions of the Royal Society of Tropical Medicine and Hygiene
|January 1, 1987
PubMed
Summary

Bayes' theorem helps diagnose enteric fever using clinical signs and lab results, even without cultures. This method accurately identifies enteric fever, aiding diagnosis when cultures are unavailable or negative.

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Area of Science:

  • Infectious Diseases
  • Clinical Diagnostics
  • Bayesian Statistics

Background:

  • Enteric fever diagnosis often relies on blood or stool cultures, which can be unavailable or yield false negatives.
  • Clinical and laboratory features are known to aid in enteric fever diagnosis, but their discriminative power needs objective quantification.

Purpose of the Study:

  • To apply Bayes' theorem for calculating the probability of enteric fever in patients with undiagnosed fever.
  • To identify key clinical and laboratory features that effectively discriminate enteric fever from other febrile illnesses.

Main Methods:

  • Bayes' theorem was utilized to assess the probability of enteric fever in 260 patients with undiagnosed fever.
  • 19 clinical and laboratory events were compared between 110 enteric fever patients and 150 patients with other fever causes.

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  • Prospective probability calculations were performed using a subset of discriminating events.

    • Main Results:

    • Eight clinical and laboratory events were significantly more frequent in enteric fever patients.
    • Using 6 discriminating events, diagnostic specificity was 0.80 and diagnostic sensitivity was 0.92.
    • Inclusion of all 19 events did not improve diagnostic accuracy.

    Conclusions:

    • A limited set of clinical and laboratory features can objectively differentiate enteric fever.
    • Probabilistic calculation using these features aids enteric fever diagnosis, especially when cultures are problematic.
    • This approach offers a valuable tool for managing undiagnosed fever cases in resource-limited settings.