Identifying associations in Escherichia coli antimicrobial resistance patterns using additive Bayesian networks

Ludwig, Antoinette; Berthiaume, Pierre; Boerlin, Patrick; Gow, Sheryl; Léger, David; Lewis, Fraser

doi:10.1016/j.prevetmed.2013.02.005

Cited by 33 publications

(32 citation statements)

References 38 publications

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“…These findings are consistent with the phenomenon of genetic capitalism where the progeny of bacteria with at least one advantageous mutation tend to acquire other additional advantageous traits over time via recombination and HGT [6]. The relative weakness and infrequency of negative partial correlations compared to positive partial correlations is also consistent with the patterns seen in the resistance relationship networks of previous studies [22, 49]. One application of R-nets and similar networks is to identify pairs or larger groups of collaterally susceptible antibiotics to create a selection inversion: a reduction in overall AMR via strategic antibiotic use [21, 22].…”

Section: Discussionsupporting

confidence: 89%

“…We chose evaluate joint distributions of log-transformed MIC values, but it is common practice to dichotomize MIC values into susceptible or resistant categories based on breakpoints when analyzing resistance data [14, 49, 54]. The transformed AMR data contain more information than dichotomized results, and therefore analyses of the continuous data is more powerful than similar analyses of dichotomized data [55, 56].…”

Section: Discussionmentioning

confidence: 99%

“…coli [22, 49, 62]. Directional edges represent types of causal relationships in the system they represent.…”

Section: Discussionmentioning

confidence: 99%

“…In some cases, it is appropriate to assign directionality to AMR relationships, e.g., collateral resistance generated by in vitro selection for resistance to individual drugs [22] or the association between genes and phenotypical resistances [62]. In many other situations, including observational or surveillance studies of AMR where antimicrobial use is unknown, causality of an AMR relationship, if any exists, must be assumed [49]. In contrast, Markov networks are undirected and have no implied causality, which avoids the risk of incorrect assumptions regarding cause and effect.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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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 β-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.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

“…coli [22, 49, 62]. Directional edges represent types of causal relationships in the system they represent.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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“…Bivariate and multivariate probit models (Agga et al, 2014(Agga et al, , 2015 for multivariate analysis of multiple binary outcomes, and multivariate linear regression model for multiple quantitative outcomes (Agga et al, 2015) were previously applied for the analysis of AMR data. Other multivariate approaches (Agga, 2013) that were used for the analysis of AMR data include cluster analysis (Berge et al, 2003;Alali et al, 2010), factor analysis (Wagner et al, 2003) and more recently Bayesian networks (Ludwig et al, 2013;Ward and Lewis, 2013).…”

Section: Introductionmentioning

confidence: 99%

Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Agga

Scott

2015

Preventive Veterinary Medicine

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a b s t r a c tStatistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR] = 1.03; 95% CI = 0.73-1.47) and CTC (Prop OR = 1.1; 95% CI = 0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated.Published by Elsevier B.V.

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Comparison between generalized linear modelling and additive Bayesian network; identification of factors associated with the incidence of antibodies against Leptospira interrogans sv Pomona in meat workers in New Zealand

et al. 2017

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Identifying associations in Escherichia coli antimicrobial resistance patterns using additive Bayesian networks

Cited by 33 publications

References 38 publications

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

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

Use of generalized ordered logistic regression for the analysis of multidrug resistance data

Comparison between generalized linear modelling and additive Bayesian network; identification of factors associated with the incidence of antibodies against Leptospira interrogans sv Pomona in meat workers in New Zealand

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