Evidence map›Paper›PMID 36384499›Full record

ArticleBMC infectious diseases2022

Effect of a real-time automatic nosocomial infection surveillance system on hospital-acquired infection prevention and control.

Ruiling Wen, Xinying Li, Tingting Liu, Guihong Lin

Abstract read
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Enhancing Agent-Based Models with Real-Time Movement Data to Assess Impacts of Biosecurity Interventions on Disease Exposure in Healthcare Settings.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025
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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

4 authors.

Ruiling WenDepartment of Infection Management, Huizhou First People's Hospital, No. 20, Sanxin South Road, Jiangbei, Huicheng District, Huizhou, Guangdong, 516003, People's Republic of China. ruiling622@126.com.
Xinying LiDepartment of Infection Management, Huizhou First People's Hospital, No. 20, Sanxin South Road, Jiangbei, Huicheng District, Huizhou, Guangdong, 516003, People's Republic of China.
Tingting LiuDepartment of Infection Management, Huizhou First People's Hospital, No. 20, Sanxin South Road, Jiangbei, Huicheng District, Huizhou, Guangdong, 516003, People's Republic of China.
Guihong LinDepartment of Infection Management, Huizhou First People's Hospital, No. 20, Sanxin South Road, Jiangbei, Huicheng District, Huizhou, Guangdong, 516003, People's Republic of China.

Funding

the Science and Technology Project of Huizhou, Guangdong Province, China 2020Y060
6 · The paper itself

Abstract

backgroundThe systematic collection of valid data related to hospital-acquired infections (HAIs) is considered effective for nosocomial infection prevention and control programs. New strategies to reduce HAIs have recently fueled the adoption of real-time automatic nosocomial infection surveillance systems (RT-NISSs). Although RT-NISSs have been implemented in some hospitals for several years, the effect of RT-NISS on HAI prevention and control needs to be further explored.

methodsA retrospective, descriptive analysis of inpatients from January 2017 to December 2019 was performed. We collected hospital-acquired infection (HAI) cases and multidrug resistant organism (MDRO) infection cases by traditional surveillance in period 1 (from January 2017 to December 2017), and these cases were collected in period 2 (from January 2018 to December 2018) and period 3 (from January 2019 to December 2019) using a real-time nosocomial infection surveillance system (RT-NISS). The accuracy of MDRO infection surveillance results over the 3 periods was examined. The trends of antibiotic utilization rates and pathogen culture rates in periods 2 and 3 were also analysed.

resultsA total of 114,647 inpatients, including 2242 HAI cases, were analysed. The incidence of HAIs in period 2 was significantly greater than that in period 1 (2.28% vs. 1.48%, χ

conclusionThe adoption of a RT-NISS to adequately and accurately collect HAI cases is useful to prevent and control HAIs. Furthermore, RT-NISSs improve accuracy in MDRO infection case reporting, which can timely and accurately guide and supervise clinicians in implementing MDRO infection prevention and control measures.

Indexed as

Cross InfectionUrinary Tract InfectionsHospitalsHumansInfection ControlRetrospective StudiesHospital-acquired infectionsInfection controlInfections surveillanceMultidrug resistant organisms

Identifiers

PMID36384499
PMCPMC9670380

What OpenQuestion holds

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Registered trials

None linked

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.