Easily available adjustment criteria for the comparison of antibiotic consumption in a hospital setting: experience in France
Easily available adjustment criteria for the comparison of antibiotic consumption in a hospital setting: experience in France
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DOI：
10.1111/j.1469-0691.2009.02920.x
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发表时间：
2010-06-01
影响因子：
14.2
通讯作者：
Rogues, A. M.
中科院分区：
文献类型：
--
作者：
Amadeo, B.;Dumartin, C.;Rogues, A. M.
法国鼓励医院监测抗生素消耗（AbC），众所周知，这在医院之间是不同的。目前研究的目的是确定相关和容易获得的调整标准，以便制定基准。我们使用2005年的回顾性数据分析了位于法国西南部和海外部门的34家公立非教学医院和43家私立医院的数据。本研究调查了AbC（表示为每1000患者日（DDD/1000 PD）或每100次入院（DDD/100入院）的定义日剂量）与静脉中心静脉导管数量、菌血症发作次数和各种医院特征之间的关系。使用多重线性分析来检验该关系。公立医院的总AbC中位数为395 DDD/1000 PD（范围，196-737）和341 DDD/100入院（范围，180-792）。在私立医院，这是422 DDD/1000 PD（范围，113-717）和212 DDD/100入院（范围，38-510）。公立医院的最佳模型包括手术、重症监护和内科病房的PD比例，并解释了以DDD/1000 PD表示的AbC变异性的84%。对于私立医院，手术和内科病房的平均住院时间和PD比例解释了AbC（DDD/100入院）68%的变异性。总体而言，法国的经验表明，医院之间比较的相关调整标准很容易获得。重要的是，每个国家建立自己的模型，考虑到医院系统的内在特点，并考虑到两个指标（DDD/1000 PD或DDD/100入院），以设计最佳模型。
Hospitals in France are encouraged to monitor antibiotic consumption (AbC) and it is known that this differs among hospitals. The aim of the current study was to identify relevant and easily available adjustment criteria for the purpose of benchmarking. We analysed data from 34 public non-teaching hospitals and 43 private hospitals located in south-western France and overseas departments using retrospective data from 2005. This study investigated the relationship between AbC expressed as defined daily doses per 1000 patient-days (DDD/1000 PDs) or per 100 admissions (DDD/100 admissions) and the number of venous central lines, the number of episodes of bacteraemia and various hospital characteristics. The relationship was tested using multiple linear analyses. The median total AbC in public hospitals was 395 DDD/1000 PDs (range, 196-737) and 341 DDD/100 admissions (range, 180-792). In private hospitals this was 422 DDD/1000 PDs (range, 113-717) and 212 DDD/100 admissions (range, 38-510). The best model for public hospitals included the proportion of PDs in surgery, intensive care and medical wards and explained 84% of the variability in AbC expressed as DDD/1000 PDs. For private hospitals, the mean length of stay and the proportion of PDs in surgery and medical wards explained 68% of the variability in AbC expressed as DDD/100 admissions. Overall, this French experience shows that relevant adjustment criteria for the comparison among hospitals are easily available. It is important that each country establish its own model considering the intrinsic peculiarities of the hospital system and taking into account both indicators (DDD/1000 PDs or DDD/100 admissions) to design the best model.