Objectives: One of the interventions to reduce risk of central line associated bloodstream infection (CLABSI) is routine replacement of the intravenous administration sets. Guidelines advises a time interval that ranges between four and seven days. However many hospitals replace intravenous administration sets every four days to prevent CLABSI.
Research Methodology: In this single centre retrospective study we analysed whether the extension of the time interval from four to seven days for routine replacement of intravenous administration sets had impact on the incidence of CLABSI and colonization of the central venous catheter. Secondary outcomes were the effects on nursing workload, material use and costs.
Results: In total, 1,409 patients with 1,679 central lines were included. During the pre-intervention period 2.8 CLABSI cases per 1,000 catheter days were found in comparison with 1.3 CLABSI cases per 1,000 catheter days during the post-intervention period. The rate difference between the groups was 1.52 CLABSI cases per 1,000 catheter days (95% CI: -0.50 to +4.13, p = 0.138). The intervention resulted in a saving of 345 intravenous single use plastic administration sets and 260 hours nursing time, and reduced cost with an estimate of at least 17.250 Euros.
Conclusion: Extension of the time interval from four to seven days for routine replacement of intravenous administration sets did not negatively affect the incidence of CLABSI.
Implications For Clinical Practice: Additional benefits of the prolonged time interval were saving of nursing time by avoiding unnecessary routine procedures, the reducing of waste because of reducing the use of disposable materials and healthcare costs.
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http://dx.doi.org/10.1016/j.iccn.2023.103446 | DOI Listing |
J Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFPLoS One
January 2025
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, Egypt.
The Weibull distribution is an important continuous distribution that is cardinal in reliability analysis and lifetime modeling. On the other hand, it has several limitations for practical applications, such as modeling lifetime scenarios with non-monotonic failure rates. However, accurate modeling of non-monotonic failure rates is essential for achieving more accurate predictions, better risk management, and informed decision-making in various domains where reliability and longevity are critical factors.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.
View Article and Find Full Text PDFAppl Environ Microbiol
January 2025
Office of Applied Science, Center for Veterinary Medicine, U.S. Food and Drug Administration, Laurel, Maryland, USA.
As a diverse and complex food matrix, the animal food microbiota and repertoire of antimicrobial resistance (AMR) genes remain to be better understood. In this study, 16S rRNA gene amplicon sequencing and shotgun metagenomics were applied to three types of animal food samples (cattle feed, dry dog food, and poultry feed). ZymoBIOMICS mock microbial community was used for workflow optimization including DNA extraction kits and bead-beating conditions.
View Article and Find Full Text PDFJMIR Form Res
January 2025
1, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.
Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023.
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