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The role of procalcitonin in a decision tree for prediction of bloodstream infection in febrile patients

  1. R. P. H. Peters1,2,
  2. J. W. R. Twisk3,
  3. M. A. Van Agtmael1,
  4. A. B. J. Groeneveld4

Article first published online: 14 AUG 2006

DOI: 10.1111/j.1469-0691.2006.01556.x

Issue

Clinical Microbiology and Infection

Clinical Microbiology and Infection

Volume 12, Issue 12, pages 1207–1213, December 2006

Additional Information

How to Cite

Peters, R. P. H., Twisk, J. W. R., Van Agtmael, M. A. and Groeneveld, A. B. J. (2006), The role of procalcitonin in a decision tree for prediction of bloodstream infection in febrile patients. Clinical Microbiology and Infection, 12: 1207–1213. doi: 10.1111/j.1469-0691.2006.01556.x

Author Information

  1. 1

    Department of Internal Medicine

  2. 2

    Department of Medical Microbiology & Infection Control

  3. 3

    Department of Clinical Epidemiology and Biostatistics

  4. 4

    Department of Intensive Care, VU University Medical Centre, Amsterdam, The Netherlands

*Corresponding author and reprint requests: A. B. J. Groeneveld, VU University Medical Centre, Department of Intensive Care, PO Box 7057, 1007 MB Amsterdam, The Netherlands E-mail: johan.groeneveld@vumc.nl

Publication History

  1. Issue published online: 14 AUG 2006
  2. Article first published online: 14 AUG 2006
  3. Original Submission: 5 January 2006; Revised Submission: 11 April 2006; Accepted: 8 June 2006

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Keywords:

  • Bloodstream infection;
  • CART analysis;
  • decision tree;
  • febrile patients;
  • prediction;
  • procalcitonin

Abstract

Bloodstream infection (BSI) in febrile patients is associated with high mortality. Clinical and laboratory variables, such as procalcitonin (PCT), may predict BSI and help decision-making concerning empirical treatment. This study compared two models for prediction of BSI, and evaluated the role of PCT vs. clinical variables, collected daily in 300 consecutive febrile inpatients, for 48 h after onset of fever. Multiple logistic regression (MLR) and classification and regression tree (CART) models were compared for discriminatory power and diagnostic performance. BSI was present in 17% of cases. MLR identified the presence of intravascular devices, nadir albumin and thrombocyte counts, and peak temperature, respiratory rate and leukocyte counts, but not PCT, as independent predictors of BSI. In contrast, a peak PCT level of >2.45 ng/mL was the principal discriminator in the decision tree based on CART. The latter was more accurate (94%) than the model based on MLR (72%; p <0.01). Hence, the presence of BSI in febrile patients is predicted more accurately and by different variables, e.g., PCT, in CART analysis, as compared with MLR models. This underlines the value of PCT plus CART analysis in the diagnosis of a febrile patient.

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