Dataset Information

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

ABSTRACT: Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and communicate joint resistance distributions. Markov networks are a class of graphical models that define connections, or edges, between pairs of variables with non-zero partial correlations and are used here to describe AMR resistance relationships. The graphical least absolute shrinkage and selection operator is used to estimate sparse Markov networks from AMR surveillance data. The method is demonstrated using a subset of Escherichia coli isolates collected by the National Antimicrobial Resistance Monitoring System between 2004 and 2012 which included AMR results for 16 drugs from 14418 isolates. Of the 119 possible unique edges, 33 unique edges were identified at least once during the study period and graphical density ranged from 16.2% to 24.8%. Two frequent dense subgraphs were noted, one containing the five ?-lactam drugs and the other containing both sulfonamides, three aminoglycosides, and tetracycline. Density did not appear to change over time (p = 0.71). Unweighted modularity did not appear to change over time (p = 0.18), but a significant decreasing trend was noted in the modularity of the weighted networks (p < 0.005) indicating relationships between drugs of different classes tended to increase in strength and frequency over time compared to relationships between drugs of the same class. The current method provides a novel method to study the joint resistance distribution, but additional work is required to unite the underlying biological and genetic characteristics of the isolates with the current results derived from phenotypic data.

SUBMITTER: Love WJ

PROVIDER: S-EPMC5112851 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

Love William J WJ Zawack Kelson A KA Booth James G JG Grӧhn Yrjo T YT Lanzas Cristina C

PLoS computational biology 20161116 11

Surveillance of antimicrobial resistance (AMR) is an important component of public health. Antimicrobial drug use generates selective pressure that may lead to resistance against to the administered drug, and may also select for collateral resistances to other drugs. Analysis of AMR surveillance data has focused on resistance to individual drugs but joint distributions of resistance in bacterial populations are infrequently analyzed and reported. New methods are needed to characterize and commun ...[more]

PMID: 27851767

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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:Antimicrobial resistance (AMR) is a major public health threat of global proportions, which has the potential to lead to approximately ten million deaths per year by 2050. Pressured by this wicked problem, in 2014, the World Health Organization launched a call for member states to share AMR data through the implementation of the Global Antimicrobial Resistance Surveillance System (GLASS), to appropriately scale and monitor the general situation world-widely. In 2017, Brazil joined GLASS and, in 2018, started its own national antimicrobial surveillance program (BR-GLASS) to understand the impact of resistance in the country. We compiled data obtained from the complete routine of three hospitals' microbiology labs during the year of 2018. This pilot data sums up to 200,874 antimicrobial susceptibility test results from 11,347 isolates. It represents 119 different microorganisms recovered from 44 distinct types of clinical samples. Specimens came from patients originating from 301 Brazilian cities, with 4,950 of these isolates from presumed Healthcare-Associated Infections (HAIs) and the other 6,397 community-acquired cases. The female population offered 58% of the collected samples, while the other 42% were of male origin. The urinary tract was the most common topography (6,372/11,347 isolates), followed by blood samples (2,072/11,347). Gram-negative predominated the bacterial isolates: Escherichia coli was the most prevalent in general, representing 4,030 isolates (89.0% of these from the urinary tract). Coagulase-negative Staphylococci were the most prevalent bacteria in blood samples. Besides these two species, the ESKAPE group have consolidated their prevalence. Regarding drug susceptibility results, 141,648 (70.5%) were susceptible, 9,950 (4.9%) intermediate, and 49,276 (24.5%) resistant. Acinetobacter baumannii was the most worrisome microorganism, with 65.3% of the overall antimicrobial susceptibility tests showing resistance, followed by ESBL-producing Klebsiella pneumoniae, with a global resistance rate of 59%. Although this is a pilot project (still limited to one state), this database shows the importance of a nation-wide surveillance program,[153mm][-12mm] Q14 especially considering it already had patients coming from 301 distinct counties and 18 different states. The BR-GLASS Program is an ongoing project that intends to encompass at least 95 hospitals distributed in all five geographical regions in Brazil within the next 5 years.

Dataset Information

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

Publications

Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012.

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