title: Predicting febrile neutropenic patients at low risk using the MASCC score: does bacteremia matter?
abstract: BACKGROUND
  Febrile neutropenic cancer patients represent a heterogeneous population with a limited proportion at risk of serious medical complications. The Multinational Association for Supportive Care in Cancer (MASCC) score has been developed and validated for identifying low-risk patients at the onset of febrile neutropenia. Since bacteremia, although not documented at baseline, is a predictor of pejorative outcome, the purpose of this study was to investigate the possible interaction between the MASCC score and bacteremic status and to assess whether, assuming that bacteremic status could be predicted at onset of febrile neutropenia, adding bacteremia as a covariate in a risk model would improve the accuracy of low-risk patients identification.
  
  
  METHODS
  Two consecutive multicentric observational studies were carried out from 1994 till 2005 involving 2,142 febrile neutropenic patients. The study data bases were retrospectively used for the present analysis.
  
  
  RESULTS
  A predictive value was found for the MASCC score in all strata obtained by stratification for the bacteremic status with odds ratios for successful outcome being, in patients with a score ≥21, respectively, 6.06 (95%CI: 4.51-8.15), 3.42 (95%CI: 1.95-5.98), and 6.04 (95%CI: 3.01-12.09) in patients without bacteremia, gram-positive bacteremia, and gram-negative bacteremia. No interaction between the MASCC score and the bacteremic status was present. A clinical prediction rule integrating the MASCC score and the bacteremic status was not helpful in improving the identification of low-risk patients. This rule may then be used in a general population of patients with febrile neutropenia without having concerns for a lower predictive value in bacteremic patients.
  
  
  CONCLUSIONS
  Our results suggest that the knowledge, provided we could find a model to predict it at fever onset, of a bacteremic etiology of the fever would be of little additional value to the MASCC score when attempting to identify low-risk patients.
authors.affiliation: Data Centre Institut Jules Bordet, Rue Héger-Bordet, 1, 1000, Brussels, Belgium. marianne.paesmans@bordet.be
authors.forename: Marianne
authors.initials: M
authors.lastname: Paesmans
authors.forename: Jean
authors.initials: J
authors.lastname: Klastersky
authors.forename: Johan
authors.initials: J
authors.lastname: Maertens
authors.forename: Aspasia
authors.initials: A
authors.lastname: Georgala
authors.forename: Frédérique
authors.initials: F
authors.lastname: Muanza
authors.forename: Mickael
authors.initials: M
authors.lastname: Aoun
authors.forename: Augustin
authors.initials: A
authors.lastname: Ferrant
authors.forename: Bernardo
authors.initials: B
authors.lastname: Rapoport
authors.forename: Ken
authors.initials: K
authors.lastname: Rolston
authors.forename: Lieveke
authors.initials: L
authors.lastname: Ameye
mesh_terms: D019540:Area Under Curve; D016001:Confidence Intervals; D005260:Female; D005334:Fever / Q000175:diagnosis* / Q000473:pathology; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009503:Neutropenia / Q000175:diagnosis* / Q000473:pathology; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013223:Statistics as Topic