1Department of Emergency Medicine,National Taiwan University Hospital,College of Medicine,National Taiwan University,Taipei,Taiwan.
2Department of Internal Medicine,National Taiwan University Hospital,College of Medicine,National Taiwan University,Taipei,Taiwan.
Infect Control Hosp Epidemiol. 2017 Oct;38(10):1216-1225. doi: 10.1017/ice.2017.178. Epub 2017 Sep 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.
从社区患者中分离出耐多药革兰氏阴性菌（MDR-GNB）的情况越来越多。因此需要建立一种预测模型，对入院时 MDR-GNB 定植和感染风险进行分层，以改善患者的治疗效果。
本研究采用两阶段前瞻性研究，分别纳入了 995 例和 998 例急诊科患者。MDR-GNB 定植定义为在监测或早期（≤48 小时）临床培养中分离出对 3 种或 3 种以上抗生素耐药的菌株。
我们使用第一阶段研究中 995 例患者的临床和微生物学数据，建立并验证了一种 MDR-GNB 定植预测评分模型，其中 122 例患者（12.3%）为 MDR-GNB 定植。我们确定了 5 个独立的预测因素：年龄>70 岁（优势比[OR]，1.84[95%置信区间（CI），1.06-3.17]；1 分）、居住在长期护理机构（OR，3.64[95%CI，1.57-8.43]；3 分）、有脑血管意外病史（OR，2.23[95%CI，1.24-4.01]；2 分）、1 个月内住院（OR，2.63[95%CI，1.39-4.96]；2 分）和近期使用抗生素（OR，2.18[95%CI，1.16-4.11]；2 分）。该模型在推导集和验证集中具有良好的区分度（ROC 曲线下面积分别为 0.75 和 0.80），低危和高危 MDR-GNB 定植的最佳截断值分别为<4 分和≥4 分。将该模型应用于第二阶段的 998 例患者，可成功地将住院期间 MDR-GNB 感染的风险在低危和高危组之间进行分层（概率分别为 0.02 和 0.12，对数秩检验，P<.001）。
本研究建立了一种模型，以优化启动抗菌治疗的决策，并采取感染控制干预措施，以降低 MDR-GNB 带来的威胁。
