1222 infection control & hospital epidemiology october 2017, vol. 38, no. 10


figure 1. Percentage of multidrug-resistant gram-negative bacteria (MDR-GNB) colonization or infection among study patients, according to the score value of the prediction model.


the patients in the second stage of the study (Table 1). Of the 47 patients with positive clinical cultures for MDR-GNB isolates, 44 showed evidence of clinical infection. When the COP model was applied to the second-stage


patient set, the model showed satisfactory predictive perfor- mance in stratifying the risk of developing MDR-GNB infec- tion during hospitalization (AUC, 0.80 [95% CI, 0.74–0.87]) (Figure 1). High-risk patients (with scores ≥4 points) had a significantly higher cumulative probability of developing a MDR-GNB infection during hospitalization than low-risk patients (with scores<4 points) (log-rank test; P<.001) (Figure 2).


discussion


figure 2. Cumulative probability of occurrence of multidrug- resistant gram-negative bacterial (MDR-GNB) infection during hospitalization among 998-second stage study patients.


exposures, had a higher percentage of malignant or end-stage renal disease, and exhibited variation in the positive previous VRE culture history (Table 2). Only 47 (4.7%) patients had at least 1 positive MDR-GNB culture from clinical specimens throughout the entire hospitalization course. Klebsiella pneu- moniae (18 of 998; 1.8%) and Acinetobacter baumannii (11 of 998; 1.1%) were the predominant MDR-GNB isolates among


In this study, we developed and validated a scoring system, using clinical variables that were easily obtained upon ED admission to predict patient at risk of MDR-GNB coloniza- tion. Our prediction model effectively stratified MDR-GNB colonization risk among patients presenting from the com- munity without requiring prior culture results. In addition, our prediction model further showed a satisfactory degree of discrimination in stratifying patients with differences in the likelihood of contracting a MDR-GNB infection during hos- pitalization. Therefore, our prediction model is useful for first- line physicians and infection control specialists and can aid in the decision-making process regarding initial antimicrobial therapy or implementation of specific interventions to prevent the spread of antimicrobial-resistant organisms in the hospital. Antimicrobial-resistant GNB are important in the etiology


of nosocomial infections, accounting for a substantial pro- portion, and they pose important therapeutic challenges for first-line physicians.27–29 Although community-acquired


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