Leverstein-van Hall Maurine A, Blok Hetty E M, Paauw Armand, Fluit Ad C, Troelstra Annet, Mascini Ellen M, Bonten Marc J M, Verhoef Jan
Eijkman-Winkler Institute for Microbiology, Infectious Disease and Inflammation, University Hospital Utrecht, Rm. G 04.614, PO 855000, 3508 GA Utrecht, The Netherlands.
J Clin Microbiol. 2006 Feb;44(2):518-24. doi: 10.1128/JCM.44.2.518-524.2006.
A hospital-wide increase in the number of patients with aminoglycoside-resistant Enterobacter cloacae (AREC) isolated from clinical cultures was detected in December 2002 using a classical surveillance system (CSS). CSS refers to a strategy based on the recognition of an increased incidence of a species with a particular antibiogram at certain wards in a limited period. Since clonal spread was suspected, hospital records were reviewed for E. cloacae culture-positive patients. Based upon genotyping of 139 clinical E. cloacae isolates from 80 patients, it was concluded that 53 patients had had clinical cultures with a single AREC clone since April 2001. Determinants for unnoticed spread were investigated retrospectively, as was the possibility that a computer-assisted surveillance method would have detected this outbreak at an earlier stage. Determinants associated with late detection of clonal spread were the following: (i) the absence of a hospital-wide increase in incidence of E. cloacae cases for 1.5 years, (ii) the long time interval between cases, (iii) the hospital-wide occurrence of new cases, due to a high number of patient transfers between wards, (iv) the large variety of clinical sites, and (v) the high variability of antibiograms (n = 33). Retrospective application of a recently described computer-assisted surveillance method as well as an "in-house"-developed algorithm resulted in earlier detection of the outbreak of 6 and 12 months, respectively. These findings suggest that computerized tools for surveillance may recognize resistance trends that are too complex to be detected by manual review and indicate the need for prospective evaluation of such algorithms.
2002年12月,采用传统监测系统(CSS)发现,从临床培养物中分离出的耐氨基糖苷类阴沟肠杆菌(AREC)患者数量在全院范围内有所增加。CSS是指一种基于在有限时间内特定病房中具有特定抗菌谱的菌种发病率增加的识别策略。由于怀疑存在克隆传播,因此对阴沟肠杆菌培养阳性患者的医院记录进行了审查。基于对80例患者的139株临床阴沟肠杆菌分离株进行基因分型,得出结论:自2001年4月以来,有53例患者的临床培养物中出现了单一的AREC克隆。回顾性调查了未被注意到的传播的决定因素,以及计算机辅助监测方法是否有可能在更早阶段发现此次疫情。与克隆传播检测延迟相关的决定因素如下:(i)1.5年内阴沟肠杆菌病例在全院范围内发病率没有增加;(ii)病例之间的时间间隔较长;(iii)由于病房之间患者转移数量众多,新病例在全院范围内出现;(iv)临床部位种类繁多;(v)抗菌谱高度可变(n = 33)。最近描述的计算机辅助监测方法以及“内部”开发的算法的回顾性应用分别使疫情提前6个月和12个月被发现。这些发现表明,计算机化监测工具可能识别出过于复杂而无法通过人工审查检测到的耐药趋势,并表明需要对这类算法进行前瞻性评估。