A prediction model for detecting patients with vancomycin-resistant Enterococci on admission to intensive care units

  • Hyeon Jeong Kim, Infection Control Unit, Korea University Medical Center, Korea
  • Young Kyung Yoon, Division of Infectious Diseases and Institute of Emerging Infectious Diseases, Korea University, Seoul, Korea, Korea
  • Kyung Sook Yang, Department of Biostatistics, Korea
  • Won Jin Lee, Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea, Korea
  • Jang Wook Sohn, Division of Infectious Diseases and Institute of Emerging Infectious Diseases, Korea University, Seoul, Korea, Korea
  • Min Ja Kim, Division of Infectious Diseases and Institute of Emerging Infectious Diseases, Korea University, Seoul, Korea, Korea

Backgrounds: The aim of the study was to develop a prediction model for detection of patients who were at risk of vancomycin-resistant enterococci (VRE) carriage on admission to intensive care units (ICUs).
Methods: A case-control study was conducted in the ICUs of a 950-bed university hospital from April 2008 to September 2010. Active surveillance rectal cultures for VRE were performed for all patients on the day of admission to the ICUs and thereafter at weekly basis. A multivariate logistic regression model was performed to identify the risk factors for VRE carriage on admission to ICUs. The discriminant ability of the models was assessed by the receiver operating characteristic curve.
Results: During the study period, 115(3.63%) of 3172 patients were found to be colonized with VRE on ICU admission The significant risk factors for carriage of VRE on ICU admission were re-admission to ICU during hospitalization (adjusted odds ratio 4.27; 95% confidence interval 1.39-13.16), pulmonary disease (9.26; 2.32-36.96), use of antibiotics within last three months (10.74; 3.83-30.07), third generation cephalosporins use (2.87; 1.03-7.99), vancomycin use (6.64;1.18-37.29) and urinary catheterization (2.83; 1.16-6.89). Given the cut off value as -2.657 resulted from an equation of a multivariate logistic regression model, the sensitivity and specificity of this prediction model were 94.8% and 75.2%, respectively (c-static, 0.916, p <0.001).
Conclusions: VRE carriage among the patients on ICU admission may increase gradually. The prediction rule developed in this study could be a useful tool to optimize VRE control.