Comparative study of phenotypic-based detection assays for carbapenemase-producing Acinetobacter baumannii with a proposed algorithm in resource-limited settings

在资源有限的环境下,对基于表型的产碳青霉烯酶鲍曼不动杆菌检测方法与所提出的算法进行比较研究

阅读:1

Abstract

The increasing incidence of carbapenem resistance in Acinetobacter baumannii is a critical concern worldwide owing to the limitations of therapeutic alternatives. The most important carbapenem resistance mechanism for A. baumannii is the enzymatic hydrolysis mediated by carbapenemases, mostly OXA-type carbapenemases (class D) and, to a lesser extent, metallo-β-lactamases (class B). Therefore, early and accurate detection of carbapenemase-producing A. baumannii is required to achieve the therapeutic efficacy of such infections. Many methods for carbapenemase detection have been proposed as effective tests for A. baumannii; however, none of them are officially recommended. In this study, three carbapenemase detection methods, namely, CarbaAcineto NP test, modified carbapenem inactivation method (mCIM), and simplified carbapenem inactivation method (sCIM) were evaluated for phenotypic detection of clinically isolated A. baumannii. The MICs of imipenem, meropenem, and doripenem were determined for 123 clinically isolated A. baumannii strains before performing three phenotypic detections. The overall sensitivity and specificity values were 89.09%/100% for the carbAcineto NP test, 71.82%/100% for sCIM, and 32.73%/33.13% for mCIM. CarbAcineto NP test and sCIM performed excellently (100% sensitivity) when both Class B and Class D carbapenemases were present in the same isolate. Based on the results, the combined detection method of sCIM and CarbAcineto NP test was proposed to detect carbapenemase-producing A. baumannii rather than a single assay, significantly increasing the sensitivity of detection to 98.18%. The proposed algorithm was more reliable and cost-effective than the CarbAcineto NP test alone. It can be easily applied in routine microbiology laboratories for developing countries with limited resources.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。