infection control & hospital epidemiology october 2017, vol. 38, no. 10 original article
Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission
Wen-Pin Tseng, MD;1 Yee-Chun Chen, MD, PhD;2,3 Bey-Jing Yang, RN, MSN;4 Shang-Yu Chen, MD;1 Jr-Jiun Lin, MD;1 Ya-Huei Huang, RN;3,4 Chia-Ming Fu, MD;1 Shan-Chwen Chang, MD, PhD;2 Shey-Ying Chen, MD1,5
objective. Isolation of multidrug-resistant gram-negative bacteria (MDR-GNB) from patients in the community has been increasingly observed. A prediction model for MDR-GNB colonization and infection risk stratification on hospital admission is needed to improve patient care.
methods. A 2-stage, prospective study was performed with 995 and 998 emergency department patients enrolled, respectively. MDR-GNB colonization was defined as isolates resistant to 3 or more classes of antibiotics, identified in either the surveillance or early (≤48 hours) clinical cultures.
results. A score-assigned MDR-GNB colonization prediction model was developed and validated using clinical and microbiological data from 995 patients enrolled in the first stage of the study; 122 of these patients (12.3%) were MDR-GNB colonized. We identified 5 independent predictors: age>70 years (odds ratio [OR], 1.84 [95% confidence interval (CI), 1.06–3.17]; 1 point), assigned point value in the model), residence in a long-term-care facility (OR, 3.64 [95% CI, 1.57–8.43); 3 points), history of cerebrovascular accidents (OR, 2.23 [95% CI, 1.24–4.01]; 2 points), hospitalization within 1 month (OR, 2.63 [95% CI, 1.39–4.96]; 2 points), and recent antibiotic exposure (OR, 2.18 [95% CI, 1.16–4.11]; 2 points). The model displayed good discrimination in the derivation and validation sets (area under ROC curve, 0.75 and 0.80, respectively) with the best cutoffs of<4 and ≥4 points for low- and high-risk MDR-GNB colonization, respectively. When applied to 998 patients in the second stage of the study, the model successfully stratified the risk of MDR-GNB infection during hospitalization between low- and high-risk groups (probability, 0.02 vs 0.12, respectively; log-rank test, P<.001).
conclusion. A model was developed to optimize both the decision to initiate antimicrobial therapy and the infection control interventions to mitigate threats from MDR-GNB.
Infect Control Hosp Epidemiol 2017;38:1216–1225
The prevalence of multidrug resistance (MDR) among gram- negative bacteria (GNB) is increasing, both in healthcare- associated and community-acquired infections, and MDR-GNB has become a major public health threat globally.1–4 Compared with infections due to antimicrobial-susceptible GNB, infec- tions due to MDR-GNB increase the risk of inappropriate initial antimicrobial therapy and consequently contribute to the increased length of hospital stay, medical costs, and mortality in patients with sepsis.5–8 These infections are especially challenging for first-line physicians, who usually make crucial decisions in treating septic patients without available microbiological test results.8,9 In addition, delayed
identification or lack of recognition of a patient with MDR- GNB colonization increases the risk of nosocomial con- tamination and spread of MDR-GNB between healthcare workers and patients, thus introducing significant infection control issues and an increased disease burden in the health- care system.10,11 Thus, the early identification of patients at risk of MDR-GNB colonization or infection at the time of admission is a critical issue, from both a clinical and an infection control perspective. Early detection optimizes treat- ment outcomes by the appropriate choice of initial anti- microbial therapy and helps prevent the nosocomial spread of MDR-GNB through implementation of specific infection
Affiliations: 1. Department of Emergency Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University,
Taipei, Taiwan; 2. Department of Internal Medicine, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan; 3. Center for Infection Control, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan; 4. Department of Nursing, National Taiwan University Hospital, Taipei, Taiwan; 5. Institute of Epidemiology and Preventive Medicine, CollegeofPublicHealth,National Taiwan University, Taipei, Taiwan.
PREVIOUS PRESENTATION: This study was presented in part at the 51st Interscience Conference on Antimicrobial Agents and Chemotherapy, Chicago, Illinois, on September 19, 2011, and at the 7th Asian Conference on Emergency Medicine in Tokyo, Japan, on October 24, 2013.
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2017/3810-0012. DOI: 10.1017/ice.2017.178 Received May 16, 2017; accepted July 17, 2017
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