Evidence map›Paper›PMID 30321629›Full record

ArticleThe Journal of hospital infection2019

Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan.

A Tsutsui, K Yahara, A Clark, K Fujimoto, S Kawakami, H Chikumi, M Iguchi, T Yagi, M A Baker, T O'Brien and 1 more

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of hospital infection, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

A TsutsuiAntimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan. Electronic address: atsutsui@nih.go.jp.
K YaharaAntimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan.
A ClarkWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Brigham and Women's Hospital, Boston, MA, USA.
K FujimotoTeikyo University Graduate School of Public Health, Tokyo, Japan.
S KawakamiAntimicrobial Resistance Research Center, National Institute of Infectious Diseases, Tokyo, Japan.
H ChikumiDivision of Infection Control and Prevention, Tottori University Hospital, Tottori, Japan.
M IguchiDepartment of Infectious Diseases, Nagoya University Hospital, Aichi, Japan.
T YagiDepartment of Infectious Diseases, Nagoya University Hospital, Aichi, Japan.
M A BakerDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health, Care Institute, Boston, MA, USA; Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
T O'BrienWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
J StellingWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

Funding

Integrating the world's microbiology laboratories into a global microbial surveillance systemU01CA207167 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI STELLING, JOHN · 2016 to 2018
$1.3M
NCI NIH HHS U01 CA207167
6 · The paper itself

Abstract

backgroundHospital outbreaks of antimicrobial-resistant (AMR) bacteria should be detected and controlled as early as possible.

aimTo develop a framework for automatic detection of AMR outbreaks in hospitals.

methodsJapan 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.

findingsData from 1031 hospitals were studied; mid-sized (200-499 beds) hospitals accounted for 60%, followed by large hospitals (≥500 beds; 24%) and small hospitals (<200 beds; 16%). More clusters were detected in large hospitals. Most of the clusters included five or fewer patients. From the in-depth analysis of 10 hospitals, ∼80% of the detected clusters were unrecognized by infection control staff because the bacterial species involved were not included in the priority pathogen list for routine surveillance. In two hospitals, clusters of more susceptible isolates were detected before outbreaks of more resistant pathogens.

conclusionWHONET-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.

Indexed as

Disease OutbreaksDrug Resistance, BacterialEpidemiological MonitoringAutomation, LaboratoryBacterial InfectionsCluster AnalysisCross InfectionHumansJapanSoftwareAntimicrobial resistanceAutomated outbreak detectionJANISOutbreakWHONET-SaTScan

Identifiers

PMID30321629
PMCPMC6461535

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.