ABSTRACT

Objectives: This study presents demographic and temporal trends in the isolation of Staphylococcus aureus in Vermont clinical microbiology laboratories and explores the use of statistical algorithms and multi-resistance phenotypes to improve outbreak detection.

Methods: Routine microbiology test results downloaded from Vermont clinical laboratory information systems were used to monitor S. aureus antimicrobial resistance trends. The integrated WHONET-SaTScan software used multi-resistance phenotypes to identify possible acute outbreaks with the space-time permutation model and slowly emerging geographic clusters using the spatial-only multinomial model.

Results: Data were provided from seven hospital laboratories from 2012 to 2018 for 19,224 S. aureus isolates from 14,939 patients. Statistically significant differences (p ≤ 0.05) in methicillin-resistant S. aureus (MRSA) isolation were seen by age group, specimen type, and health-care setting. Among MRSA, multi-resistance profiles permitted the recognition and tracking of 6 common and 21 rare ‘phenotypic clones.’ We identified 43 acute MRSA clusters and 7 significant geographic clusters (p ≤ 0.05).

Conclusions: There was significant heterogeneity in MRSA strains between facilities and the use of multi-resistance phenotypes facilitated the recognition of possible outbreaks. Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging resistance threats.

摘要

目的：本研究展示了佛蒙特州临床微生物学实验室金黄色葡萄球菌分离的人口统计学和时间趋势，并探讨了使用统计算法和多耐药表型来改进爆发检测。

方法：使用从佛蒙特州临床实验室信息系统下载的常规微生物学检测结果来监测金黄色葡萄球菌抗微生物药物耐药性趋势。集成的 WHONET-SaTScan 软件使用多抗性表型通过时空排列模型识别可能的急性爆发，并使用仅空间多项式模型识别缓慢出现的地理集群。

结果： 2012 年至 2018 年，来自 7 个医院实验室的数据来自 14,939 名患者的 19,224 株金黄色葡萄球菌分离株。不同年龄组、样本类型和医疗机构在耐甲氧西林金黄色葡萄球菌 (MRSA) 分离中存在统计学显着差异 (p ≤ 0.05)。在 MRSA 中，多抗性谱允许识别和跟踪 6 个常见和 21 个罕见的“表型克隆”。我们确定了 43 个急性 MRSA 集群和 7 个重要的地理集群（p ≤ 0.05）。

结论：设施之间的 MRSA 菌株存在显着的异质性，多抗性表型的使用促进了对可能爆发的识别。利用常规临床微生物学数据和免费软件工具对抗菌素耐药性进行全面电子监测，可及早识别和跟踪新出现的耐药性威胁。