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预测住院时多重耐药革兰氏阴性菌定植和相关感染。

Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission.

机构信息

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.

出版信息

Infect Control Hosp Epidemiol. 2017 Oct;38(10):1216-1225. doi: 10.1017/ice.2017.178. Epub 2017 Sep 5.

Abstract

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.

摘要

目的

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。

方法

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。

结果

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)。

结论

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。

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