ArticleFrontiers in public health2024
Uses of a real-time automatic nosocomial infection surveillance system to support prevention and control of hospital-acquired infections in the ICU.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Beyond the Reported Numbers:Antibiotics (Basel, Switzerland) · 2026Article
- Review
- Efficacy of artificial saliva in preventing oral changes in patients admitted to the ICU: a controlled clinical trial.Clinical oral investigations · 2026Article
- Pathogen profiles and risk factors of hospital-acquired infections in traumatic brain injury patients.Frontiers in public health · 2026Article
- Update on infection prevention in the ICU.Current opinion in critical care · 2025Review
- Assessment of infection prevention and control management in obstetrics departments in secondary and tertiary hospitals : A cross-sectional study from Guiyang city, China, 2023.BMC infectious diseases · 2025Article
- A Hospital Infection Management Department-Led Intervention to Improve the Pathogen Submission Rate Before Antimicrobial Therapy Using a FOCUS-PDCA Model.Infection and drug resistance · 2025Article
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5 authors.
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Abstract
Introduction: The intensive care unit (ICU) caters to patients with severe illnesses or injuries who require constant medical attention. These patients are susceptible to infections due to their weak immune systems and prolonged hospital stays. This makes the ICU the specialty with the highest hospital-acquired infection (HAI) cases. The core dimension of infection prevention and control for ICUs is infection surveillance, which analyses the risk factors of HAI and implements comprehensive interventions for HAI prevention and control. Hence, this study aimed to investigate the potential risk factors for developing HAI in the ICU using real-time automatic nosocomial infection surveillance systems (RT-NISS) to surveil, and analyze the effectiveness of RT-NISS coupled with comprehensive interventions on HAI prevention and control in the ICU. Methods: A retrospective analysis was conducted using data from an RT-NISS for all inpatients in the ICU from January 2021 to December 2022. Univariate and multivariate logistic regression analyses were performed to analyse potential risk factors for HAI in the ICU. Surveillance of the prevalence proportion of HAI, the prevalence proportion of site-specific HAI, the proportion of ICU patients receiving antibiotics, the proportion of ICU patients receiving key antimicrobial combination, the proportion of HAI patients with pathogen detection, the proportion of patients with pathogen detection before antimicrobial treatment and the proportion of patients before receiving key antimicrobial combination, the utilization rate of devices and the rate of device-associated HAIs were monitored monthly by the RT-NISS. Comprehensive interventions were implemented in 2022, and we compared the results of HAIs between 2021 and 2022 to evaluate the effect of the RT-NISS application combined with comprehensive interventions on HAI prevention and control. Results: The relative risk factors, observed as being a significantly higher risk of developing HAI, were hospitalization over 2 weeks, chronic lung diseases, chronic heart diseases, chronic renal diseases, current malignancy, hypohepatia, stroke, cerebrovascular accident, severe trauma, tracheal intubation and tracheostomy and urinary catheter. By implementing comprehensive interventions depending on infection surveillance by the RT-NISS in 2022, the prevalence proportion of HAI was reduced from 12.67% in 2021 to 9.05% in 2022 (χ Conclusion: The adoption of an RT-NISS can adequately and accurately collect HAI case information to analyse the relative high-risk factors for developing HAIs in the ICU. Furthermore, implementing comprehensive interventions derived from real-time automation surveillance of the RT-NISS will reduce the risk and prevalence proportions of HAIs in the ICU.
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