Factors Associated With Bacteremia in Febrile, Granulocytopenic Cancer-patients

The objective of this investigation was to determine factors predictive of bacteraemia at presentation in febrile, granulocytopenic cancer patients in order to estimate the probability of bacteraemia in each patient, and to compare factors associated with a diagnosis of gram-positive or gram-negative bacteraemia. Retrospective analysis of two sets of data (derivation and validation sets) randomly obtained from a large prospective study was conducted in a multicentre study of febrile, granulocytopenic cancer patients admitted for empiric antibacterial therapy. Within the derivation set, prognostic factors (clinical and laboratory data) likely to be associated with a generic diagnosis of bacteraemia and with a specific diagnosis of gram-positive or gram-negative bacteraemia were analysed by means of three backward, stepwise, logistic regression analyses. The predictive probability of bacteraemia was calculated using the logistic equation. The discriminating ability of the model in predicting bacteraemia was evaluated in the derivation and validation sets using receiver-operating characteristic curves. The predictive probability of gram-positive or gram-negative bacteraemia was not calculated. In the derivation set, 157 of 558 episodes (28%) were microbiologically documented bacteraemias. Predicting factors were antifungal prophylaxis, duration of granulocytopenia before fever, platelet count, highest fever, shock and presence and location of initial signs of infection. The variables institution, antibacterial prophylaxis and underlying disease showed borderline associations with bacteraemia. Shock was associated with gram-negative bacteraemia, while signs of infection at catheter site were predictive of gram-positive bacteraemia. Quinolone prophylaxis was negatively associated with gram-negative bacteraemia. When tested in the validation set, the model was poorly predictive, although a small subgroup of episodes (representing only 16% of the total sample size) with low risk of bacteraemia was identified. Factors predictive of bacteraemia can be identified, with discrimination between gram-positive and gram-negative aetiology. Further studies are warranted in order to improve the discriminant ability of the model.