Nosocomial infections in brazilian pediatric patients: using a decision tree to identify high mortality groups

• Main Authors: Julia M.M. Lopes, Eugenio M.A. Goulart, Arminda L. Siqueira, Inara K. Fonseca, Marcus V.S. de Brito, Carlos E.F. Starling
• Format: Article
• Language: English
• Published: Elsevier
• Series: Brazilian Journal of Infectious Diseases
• Subjects: Epidemiology; healthcare quality; mortality; nosocomial infection; pediatric hospital
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702009000200008&lng=en&tlng=en

Nosocomial infections (NI) are frequent events with potentially lethal outcomes. We identified predictive factors for mortality related to NI and developed an algorithm for predicting that risk in order to improve hospital epidemiology and healthcare quality programs. We made a prospective cohort NI surveillance of all acute-care patients according to the National Nosocomial Infections Surveillance System guidelines since 1992, applying the Centers for Disease Control and Prevention 1988 definitions adapted to a Brazilian pediatric hospital. Thirty-eight deaths considered to be related to NI were analyzed as the outcome variable for 754 patients with NI, whose survival time was taken into consideration. The predictive factors for mortality related to NI (p < 0.05 in the Cox regression model) were: invasive procedures and use of two or more antibiotics. The mean survival time was significantly shorter (p < 0.05 with the Kaplan-Meier method) for patients who suffered invasive procedures and for those who received two or more antibiotics. Applying a tree-structured survival analysis (TSSA), two groups with high mortality rates were identified: one group with time from admission to the first NI less than 11 days, received two or more antibiotics and suffered invasive procedures; the other group had the first NI between 12 and 22 days after admission and was subjected to invasive procedures. The possible modifiable factors to prevent mortality involve invasive devices and antibiotics. The TSSA approach is helpful to identify combinations of predictors and to guide protective actions to be taken in continuous-quality-improvement programs.