Comparative efficacy of antimicrobial dressings and securement devices

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The purpose of our study is to carry out a Bayesian network meta-analysis comparing the efficacy of different antimicrobial dressings for prevention of catheter-related blood infections (CRBSI) and rank these antimicrobial dressings for practical consideration” Dang et al (2019).

Abstract:

PURPOSE: The purpose of our study is to carry out a Bayesian network meta-analysis comparing the efficacy of different antimicrobial dressings for prevention of catheter-related blood infections (CRBSI) and rank these antimicrobial dressings for practical consideration.

METHODS: We searched the PubMed, Cochrane library, Embase, earlier relevant meta-analysis and reference lists of included studies for randomized controlled trials (RCTs) that compared dressings for prevention of CRBSI. Two authors independently extracted data from each included RCT according to a predesigned Excel spreadsheet and assessed the methodological quality of included RCTs using the Cochrane risk of bias tool. Data was analyzed using the WinBUGS (V.1.4.3) and the Stata (V.15.0).

RESULTS: Finally, 35 RCTs involving 8494 patients and evaluating 13 dressings were included. Network meta-analysis showed that transparent dressing may be the best way to prevent CRBSI. Suture and bordered polyurethane dressing might have the lowest risk of CRBSI rate per 1000 catheter-days, and sutureless securement device might lead to the lowest incidence of catheter failure.

CONCLUSIONS: This network meta-analysis indicated that transparent dressings may be selected for the prevention of CRBSI in patients with central venous catheters, which is of importance in future research. Although evidence is scant, more attention should be paid to head-to-head comparisons of the most commonly used dressings in this field.

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Reference:

Dang, F.P., Li, H.J. and Tian, J.H. (2019) Comparative efficacy of 13 antimicrobial dressings and different securement devices in reducing catheter-related bloodstream infections: A Bayesian network meta-analysis. Medicine. 98(14), p.e14940. doi: 10.1097/MD.0000000000014940.

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