OBJECTIVES The aim of the study was to measure the impact of antibiotic exposure on the acquisition of colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria (ESBL-GNB) accounting for individual- and group-level confounding using machine-learning methods. METHODS Patients hospitalized between September 2010 and June 2013 at six medical and six surgical wards in Italy, Serbia and Romania were screened for ESBL-GNB at hospital admission, discharge, antibiotic start, and after 3, 7, 15 and 30 days. Primary outcomes were the incidence rate and predictive factors of new ESBL-GNB colonization. Random forest algorithm was used to rank antibiotics according to the risk of selection of ESBL-GNB colonization in patients not colonized before starting antibiotics. RESULTS We screened 10 034 patients collecting 28 322 rectal swab samples. New ESBL-GNB colonization incidence with and without antibiotic treatment was 22/1000 and 9/1000 exposure-days, respectively. In the adjusted regression analyses, antibiotic exposure (hazard ratio (HR) 2.38; 95% CI 1.29-4.40), age 60-69 years (HR 1.19; 95% CI 1.05-1.34), and spring season (HR 1.25; 95% CI 1.14-1.38) were independently associated with new colonization. Monotherapy ranked higher als combination therapy in promoting ESBL-GNB colonization. Among monotherapy, cephalosporins ranked first followed by tetracycline (second), macrolide (fourth) and cotrimoxazole (seventh). Overall the ranking of cephalosporins was lower when used in combination. Among combinations not including cephalosporins, quinolones plus carbapenems ranked highest (eighth). Among sequential therapies, quinolones ranked highest (tenth) when prescribed within 30 days of therapy with cephalosporins. CONCLUSIONS Impact of antibiotics on selecting ESBL-GNB at intestinal level varies if used in monotherapy or combination and according to previous antibiotic exposure. These finding should be explored in future clinical trials on antibiotic stewardship interventions. CLINICAL TRIAL REGISTRATION NCT01208519.

中文翻译：

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使用机器学习方法评估抗生素暴露与定植产生广谱β-内酰胺酶的革兰氏阴性细菌之间的关联：一项多中心，前瞻性队列研究。

目的本研究的目的是测量使用机器学习在个体和群体水平上造成混淆的因素，以测定产生抗生素的广谱β-内酰胺酶革兰氏阴性菌对定植菌落的影响。方法。方法于2010年9月至2013年6月在意大利，塞尔维亚和罗马尼亚的6个医疗病房和6个外科病房住院的患者在入院，出院，开始使用抗生素以及第3、7、15和30天后进行ESBL-GNB筛查。主要结局是新的ESBL-GNB定植的发生率和预测因素。根据在开始抗生素治疗前未定植的患者选择ESBL-GNB定植的风险，使用随机森林算法对抗生素进行排名。结果我们筛选了10034例患者，收集了28322例直肠拭子样本。有和没有抗生素治疗的新ESBL-GNB定植发生率分别为22/1000和9/1000暴露天。在调整后的回归分析中，抗生素暴露（危险比（HR）2.38； 95％CI 1.29-4.40），年龄60-69岁（HR 1.19； 95％CI 1.05-1.34）和春季（HR 1.25； 95％） CI 1.14-1.38）独立地与新的殖民地相关。单一疗法在促进ESBL-GNB菌落定植方面排名较高。在单药治疗中，头孢菌素位居第一，其次是四环素（第二），大环内酯类（第四）和考特莫唑（第七）。当联合使用时，头孢菌素的总体排名较低。在不包括头孢菌素的组合中，喹诺酮类和碳青霉烯类化合物排名最高（第八位）。在序贯疗法中，喹诺酮类药物在头孢菌素治疗后30天内处方时排名最高（第十）。结论如果采用单一疗法或联合疗法，并且根据先前的抗生素暴露情况，抗生素对选择肠一级ESBL-GNB的影响会有所不同。这些发现应在未来有关抗生素管理干预的临床试验中进行探索。临床试验注册NCT01208519。