Erb Stefan, Frei Reno, Tschudin Sutter Sarah, Egli Adrian, Dangel Marc, Bonkat Gernot, Widmer Andreas F
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, University of Basel, Switzerland.
Division of Clinical Microbiology, University Hospital Basel, University of Basel, Switzerland.
Swiss Med Wkly. 2018 Nov 15;148:w14660. doi: 10.4414/smw.2018.14660. eCollection 2018 Nov 5.
Antimicrobial resistance data from surveillance networks are frequently do not accurately predict resistance patterns of urinary tract infections at the bedside.
To determine simple patient- and institution-related risk factors affecting antimicrobial resistance patterns of Escherichia coli urine isolates.
From January 2012 to May 2015 all consecutive urine samples with significant growth of E. coli (≥103 CFU/ml) obtained from a tertiary care hospital were analysed for antimicrobial susceptibility and related to basic clinical data such a patient age, ward, sample type (catheter vs non-catheter urine).
Antimicrobial susceptibility testing was available for 5246 E. coli urine isolates from 4870 patients. E. coli was most commonly resistant to amoxicillin (43.1%), cotrimoxazole (24.5%) and ciprofloxacin (17.4%). Resistance rates were low for meropenem (0.0%), fosfomycin (0.9%) and nitrofurantoin (1.5%). Significantly higher rates of resistance to ciprofloxacin (32.8 vs 15.8%) and cotrimoxazole (30.6 vs 23.9%) were found in urological patients compared with patients on other wards (p <0.01). In multivariable analysis, predictors for E. coli resistance against ciprofloxacin and cotrimoxazole were: treatment in the urological unit (odds ratio [OR] 2.04, 95% confidence interval [CI] 1.63-2.54; p <0.001 and OR 1.33, 95% CI 1.07-1.64; p = 0.010, respectively), male sex (OR 1.93, 95% CI 1.630-2.29; p <0.001 and OR 1.22, 95% CI 1.22-1.04; p = 0.015), and only to a lesser extent urine samples obtained from indwelling catheters (OR 1.30, 95% CI 1.05-1.61; p = 0.014 and OR 1.26, 95% CI 1.04-1.53; p = 0.020). Age ≥65 years was associated with higher resistance to ciprofloxacin (OR 1.42, 95% CI 1.21-1.67; p <0.001), but lower resistance to cotrimoxazole (OR 0.76, 95% CI 0.67-0.86; p <0.001).
Simple bedside patient data such as age, sex and treating hospital unit help to predict antimicrobial resistance and can improve the empirical treatment of urinary tract infections.
监测网络提供的抗菌药物耐药性数据常常无法准确预测床边尿路感染的耐药模式。
确定影响大肠杆菌尿液分离株抗菌药物耐药模式的简单的患者及机构相关风险因素。
对2012年1月至2015年5月期间从一家三级护理医院获取的所有大肠杆菌显著生长（≥103 CFU/ml）的连续尿液样本进行抗菌药物敏感性分析，并与患者年龄、病房、样本类型（导尿管尿液与非导尿管尿液）等基本临床数据相关联。
对4870例患者的5246株大肠杆菌尿液分离株进行了抗菌药物敏感性检测。大肠杆菌最常对阿莫西林（43.1%）、复方新诺明（24.5%）和环丙沙星（17.4%）耐药。美罗培南（0.0%）、磷霉素（0.9%）和呋喃妥因（1.5%）的耐药率较低。与其他病房的患者相比，泌尿外科患者对环丙沙星（32.8%对15.8%）和复方新诺明（30.6%对23.9%）的耐药率显著更高（p<0.01）。在多变量分析中，大肠杆菌对环丙沙星和复方新诺明耐药的预测因素为：在泌尿外科治疗（比值比[OR] 2.04，95%置信区间[CI] 1.63 - 2.54；p<0.001和OR 1.33，95% CI 1.07 - 1.64；p = 0.010））、男性（OR 1.93，95% CI 1.630 - 2.29；p<0.001和OR 1.22，95% CI 1.22 - 1.04；p = 0.015），以及程度较轻的从留置导尿管获取的尿液样本（OR 1.30，95% CI 1.05 - 1.61；p = 0.014和OR 1.26，95% CI 1.04 - 1.53；p = 0.020）。年龄≥65岁与对环丙沙星的较高耐药性相关（OR 1.42，95% CI 1.21 - 1.67；p<0.001），但与对复方新诺明的较低耐药性相关（OR 0.76，95% CI 0.67 - 0.86；p<0.001）。
年龄、性别和治疗科室等简单的床边患者数据有助于预测抗菌药物耐药性，并可改善尿路感染的经验性治疗。
