Dataset Information

Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

ABSTRACT:

Objective

The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy.

Methods

Retrospective cohort study including all patients admitted to three neonatal (NICU), two pediatric (PICU) and four adult ICUs between April 2006 and March 2010. Ten different resistance/antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE) in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests.

Results

Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006).

Conclusions

A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use.

SUBMITTER: Fortin E

PROVIDER: S-EPMC4692550 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

Fortin Élise É Platt Robert W RW Fontela Patricia S PS Buckeridge David L DL Quach Caroline C

PloS one 20151228 12

<h4>Objective</h4>The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy.<h4>Methods</h4>Retrospective cohort study including all patients admitted to three neonatal (NICU), two ...[more]

PMID: 26710322

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Project description:OBJECTIVE: To report the experience gained over 4 years in working with the German SARI project (Surveillance of Antimicrobial Use and Antimicrobial Resistance in Intensive Care Units), and to compare SARI with data from the Swedish STRAMA and the US AUR surveillance system. METHODS: Prospective unit and laboratory based surveillance was carried out in 40 German ICUs from 2001 through 2004. WHO 2004 definitions of defined daily doses (DDD) per 1,000 patient days (pd) were used to express antimicrobial consumption (AD). Apart from the proportion of resistant isolates (RP), the incidence density of resistant isolates (RD) was calculated on the basis of the number of resistant isolates per 1,000 pd. To determine the changes over time, the Wilcoxon signed rank test for paired samples was used. RESULTS: From 1/2001 through 12/2004, 40 ICUs provided data on 53,399 isolates, a total of 789,569 DDD and 597,592 pd. Total AD ranged from 427 to 2,798, with the median being 1,351. There was no statistically significant change in total antimicrobial use, but a statistically significant decrease was observed in the use of aminoglycosides. RD was highest for MRSA with 4.4 resistant isolates/1,000 pd followed by imipenem resistant Pseudomonas aeruginosa with 1.7 resistant isolates/1,000 pd. The corresponding RPs were 21.5% and 23.2%. Over the 4-year period (2001-2004), significant increases were seen in the RDs of third generation cephalosporin and ciprofloxacin resistant Escherichia coli. In 2004, the mean RD reached 0.28 and 1.41, respectively. In comparison, the RP of selected pathogens was highest in the US ICUs and lowest in Swedish ICUs, with the exception of imipenem resistant P. aeruginosa. CONCLUSION: Antibiotic consumption remained stable over a period of 4 years, (the mean being 1,321 DDD/1,000 pd). The same applied to the situation regarding resistance in Staphylococcus aureus, enterococci and P. aeruginosa. For most pathogens the RP was higher in SARI ICUs than in Swedish ICUs, but lower than in US ICUs.

Project description:Quantification and tracking of antimicrobial use (AMU) are key factors for the development of responsible antimicrobial stewardship programs and comparison between countries. Global finfish aquaculture growth and increased AMU creates the potential for exchange of antimicrobial resistance between aquatic and terrestrial environments, making AMU surveillance imperative for this industry. The objective of this review is to collate current literature on AMU surveillance indicators and their application to commercial finfish aquaculture production. A systematic search strategy was applied to five databases: Medline, Embase, Agricola, CAB abstracts, and Biosis. To be included, studies must report on at least one AMU surveillance indicator for use in animals. There is no single, standardized indicator suitable to report finfish aquaculture AMU. The type and availability of finfish aquaculture data presents unique considerations for AMU reporting. Ultimately, the indicator used should be fit-for-purpose to satisfy the objective of the surveillance program, motivation for comparison and provide useful information to the industry stakeholders. Finfish aquaculture total annual slaughter weight allows estimation of biomass for the population correction unit (PCU) to report annual total mg of active antimicrobial ingredient per PCU. These data are commonly reported by finfish aquaculture-producing countries, allowing for international comparisons. However, this precludes the ability to compare to terrestrial livestock where the PCU is based on animal numbers and an average treatment weight, which are not available for finfish aquaculture. The mg per adjusted PCU indicator provides an interesting alternative that incorporates the length of the marine grow-out phase for finfish, but is subject to the same limitations. The number of defined daily doses animal per animal-days-at-risk is useful but also limited by a lack of a defined average treatment weight. The concept of average treatment weight remains challenging for the industry as it does not accurately reflect the timing of actual AMU to fish in the system. The term "average biomass" is more reflective of the intent of AMU surveillance indicators. Defining an average treatment weight, or average biomass, will require industry engagement, which is crucial if AMU reporting is to be deemed credible and provide value back to the finfish aquaculture industry.

Project description:IntroductionThe preventive impact of hospital-acquired infection (HAI) surveillance is difficult to assess. Our objective was to investigate the effect of HAI surveillance disruption on ventilator-associated pneumonia (VAP) incidence.MethodsA quasi-experimental study with an intervention group and a control group was conducted between 1 January 2004 and 31 December 2010 in two intensive care units (ICUs) of a university hospital that participated in a national HAI surveillance network. Surveillance was interrupted during the year 2007 in unit A (intervention group) and was continuous in unit B (control group). Period 1 (pre-test period) comprised patients hospitalized during 2004 to 2006, and period 2 (post-test period) involved patients hospitalized during 2008 to 2010. Patients hospitalized ? 48 hours and intubated during their stay were included. Multivariate Poisson regression was fitted to ascertain the influence of surveillance disruption.ResultsA total of 2,771 patients, accounting for 19,848 intubation-days at risk, were studied; 307 had VAP. The VAP attack rate increased in unit A from 7.8% during period 1 to 17.1% during period 2 (P <0.001); in unit B, it was 7.2% and 11.2% for the two periods respectively (P = 0.17). Adjusted VAP incidence rose in unit A after surveillance disruption (incidence rate ratio = 2.17, 95% confidence interval 1.05 to 4.47, P = 0.036), independently of VAP trend; no change was observed in unit B. All-cause mortality and length of stay increased (P = 0.028 and P = 0.038, respectively) in unit A between periods 1 and 2. In unit B, no change in mortality was observed (P = 0.22), while length of stay decreased between periods 1 and 2 (P = 0.002).ConclusionsVAP incidence, length of stay and all-cause mortality rose after HAI surveillance disruption in ICU, which suggests a specific effect of HAI surveillance on VAP prevention and reinforces the role of data feedback and counselling as a mechanism to facilitate performance improvement.

Dataset Information

Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

Objective

Methods

Results

Conclusions

Publications

Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

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