A validated prediction model for intestinal vancomycin-resistant Enterococcus colonization in critically ill patients with sepsis: insights into risk stratification and clinical impact
摘要
Intestinal colonization with vancomycin-resistant Enterococcus (VRE) is a critical precursor to invasive infection. However, data on its determinants and prognostic implications in critically ill patients with sepsis remain limited. This study aimed to develop and internally validate a prediction model for intestinal VRE colonization and to assess its clinical consequences.
MethodsWe conducted a retrospective cohort study of adult patients with sepsis admitted to the intensive care unit (ICU) of a tertiary medical center in Taiwan in 2024. Multivariable logistic regression was used to identify independent predictors of intestinal VRE colonization, and two prediction models were developed and internally validated using a split-sample approach. Model performance was assessed by discrimination, calibration, and decision curve analysis. The incidences of VRE-related urinary tract infection and bacteremia within 30 days of ICU admission were compared between colonized and non-colonized patients.
ResultsAmong 250 eligible patients, 14.0% had intestinal VRE colonization. Three independent predictors were identified: the Charlson Comorbidity Index (adjusted odds ratio [aOR], 1.676; 95% confidence interval [CI], 1.268–2.214), prior exposure to cephalosporins (aOR, 2.80; 95% CI, 1.146–6.841), and enteral feeding (aOR, 5.21; 95% CI, 1.16–23.41). The final model showed good discrimination, with an AUC of 0.751 (95% CI, 0.572–0.930) in the validation cohort. Calibration analyses indicated good agreement between predicted and observed risks, and decision curve analysis showed positive net benefit across clinically relevant threshold probabilities. Patients with baseline VRE colonization had significantly higher risks of subsequent VRE-related urinary tract infection (22.9% vs. 1.4%, p < 0.001) and bacteremia (5.7% vs. 0.5%, p = 0.008).
ConclusionsWe developed and internally validated a prediction model with potential clinical utility for early risk stratification in critically ill patients with sepsis.
Clinical trial registrationClinical trial number: not applicable.