Fatores preditores para infecção ou colonização por bactérias multidroga resistentes: um estudo de caso-contole/Predicting factors for infection or colonization by multidrug-resistant bacteria in a general hospital: a case-control study

Paulo Victor Fernandes Souza Nascimento, Lucia Garcia Dantas Martins Silva, Paulo Roberto de Madureira


Nosocomial infections are frequently caused by multidrug-resistant microorganisms. In everyday clinical practices, physicians deal with a dilemma in treating serious infections: either to prescribe broad-spectrum antibiotics and contribute to increasing antibiotic resistance or to use a narrow spectrum of antimicrobials and put patients’ prognosis at risk. The aim of this study was to identify potential predictors for the harboring of multidrug-resistant bacteria and to build a clinical prediction model that can help physicians to recognize patients with different risks for infection or colonization by these microorganisms. We conducted a case-control study with all patients that performed at least one culture. Cases were defined as patients that had had a culture demonstrating a multi-resistant agent. Controls were all other patients that had at least one culture. The consensus definition from Center for Disease Control and the European Centre for Disease Prevention and Control was used to describe antibiotic multi-resistance. Traditional risk factors were evaluated as predictors. A backward logistical regression identified that an admission history of 180 days, tube feeding, length of hospital stay before culture, Charlson comorbidity index, central venous catheter, and tracheostomy are all independent predictors for patients that harbor multidrug-resistant microorganisms. The bootstrap procedure was employed to examine the internal validity. Shrinkage was utilized to correct optimism and the model was calibrated. The regression formula is described and the final model accuracy was evaluated by a receiver operating characteristic curve analysis. The area under the curve was 0.78 showing that the discriminative ability of the prediction model was good.

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