Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan.
Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan.
复制标题
DOI：
10.1016/j.jhin.2018.10.005
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Stelling J
中科院分区：
文献类型：
--
作者：
Tsutsui A;Yahara K;Clark A;Fujimoto K;Kawakami S;Chikumi H;Iguchi M;Yagi T;Baker MA;O'Brien T;Stelling J
应尽早发现和控制医院内抗生素耐药（AMR）细菌的爆发。建立自动检测医院AMR爆发的框架。日本医院感染监测（JANIS）是世界上最大的国家AMR监测系统之一。在这项研究中，提取了2011年至2016年期间JANIS数据库中的所有细菌数据。WHONET是一种用于管理微生物学数据的免费软件，SaTScan是嵌入WHONET的免费聚类检测工具，用于分析2015-2016年合格医院的数据。然后使用2011-2016年的数据对日本各地的10家代表性医院进行了手动评估和验证。研究数据来自1,031家医院;中型（200-499张床位）医院占60%，其次是大型（≥ 500张床位）医院（24%）和小型（< 200张床位）医院（16%）。大医院导致更多的集群检测。大多数集群包括5名或更少的患者。根据对10间医院的深入分析，约80%的已发现菌群未能被感染控制人员识别，原因是所涉及的细菌种类并不包括在常规监测的优先病原体名单内。特别是，在两家医院，在更耐药的病原体爆发之前检测到更敏感的分离株集群。WHONET-SaTScan可以根据高优先级AMR病原体列表之外的分离株耐药谱自动检测流行病学相关患者群。如果能够检测到更敏感的分离株集群，则可能允许在更耐药病原体爆发之前对感染控制实践进行早期干预。
Hospital outbreaks of antimicrobial-resistant (AMR) bacteria should be detected and controlled as early as possible. To develop a framework for automatic detection of AMR outbreaks in hospitals. Japan Nosocomial Infections Surveillance (JANIS) is one of the largest national AMR surveillance systems in the world. For this study, all bacterial data in the JANIS database were extracted between 2011 and 2016. WHONET, a free software for the management of microbiology data, and SaTScan, a free cluster detection tool embedded in WHONET, were used to analyse 2015–2016 data of eligible hospitals. Manual evaluation and validation of 10 representative hospitals around Japan were then performed using 2011–2016 data. Data from 1,031 hospitals were studied; mid-sized (200–499 bed) hospitals accounted for 60%, followed by large (≥ 500 beds) hospitals (24%) and small (< 200 beds) hospitals (16%). Large hospitals resulted in more clusters detected. Most of the clusters included five or fewer patients. From the in-depth analysis of 10 hospitals, approximately 80% of the detected clusters were unrecognised by infection control staff because the bacterial species involved were not included in the priority pathogen list for routine surveillance. In particular, in two hospitals, clusters of more susceptible isolates were detected before outbreaks of more resistant pathogens. WHONET-SaTScan can automatically detect clusters of epidemiologically-related patients based on isolate resistance profiles beyond lists of high-priority AMR pathogens. If clusters of more susceptible isolates can be detected, it may allow early intervention in infection control practices before outbreaks of more resistant pathogens occur.
登录
查看更多内容
在两家医院中检测与铜绿假单胞菌相关感染或定植的时间簇：Satscan和Whonet软件包的比较。
影响因子：
3.7
作者：
Lefebvre A;Bertrand X;Vanhems P;Lucet JC;Chavanet P;Astruc K;Thouverez M;Quantin C;Aho-Glélé LS
通讯作者：
Aho-Glélé LS
影响因子：
19
作者：
Natale, A.;Stelling, J.;D'Ancona, F.
通讯作者：
D'Ancona, F.
影响因子：
3.8
作者：
Vinas, Maria R.;Tuduri, Ezequiel;Galas, Marcelo
通讯作者：
Galas, Marcelo
影响因子：
4.5
作者：
Baker, Meghan A.;Huang, Susan S.;Yokoe, Deborah S.
通讯作者：
Yokoe, Deborah S.
影响因子：
15.8
作者：
Huang, Susan S.;Yokoe, Deborah S.;Platt, Richard
通讯作者：
Platt, Richard