Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive methicillin-resistant Staphylococcus aureus infections in the United States using chain graphs

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common causes of hospital- and community-acquired infections. MRSA is resistant to many antibiotics, including β-lactam antibiotics, fluoroquinolones, lincosamides, macrolides, aminoglycosides, tetracyclines, and chloramphenicol. Graphical models such as chain graphs can be used to quantify and visualize the interactions among multiple resistant outcomes and their explanatory variables. In this study, we analyzed MRSA surveillance data collected by the Centers for Disease Control and Prevention (CDC) as part of the Emerging Infections Program (EIP) using chain graphs with the objective of identifying risk factors for individual phenotypic resistance outcomes (reported as minimum inhibitory concentration, MIC) while considering the correlations among themselves. Some phenotypic resistances have low connectivity to other outcomes or predictors (e.g. tetracycline, vancomycin, doxycycline, and rifampin). Levofloxacin was the only resistant associated with healthcare use. Blood culture was the most common predictor of MIC. Patients with positive blood culture had significantly increased MIC to chloramphenicol, erythromycin, gentamicin, lincomycin, and mupirocin, and decreased daptomycin and rifampin MICs. Chain graphs show the unique and common risk factors associated with resistance outcomes.

Chain graph reduction into Power Chain Graphs

Reduction of graphs is a class of procedures used to decrease the dimensionality of a given graph in which the properties of the reduced graph are to be induced from the properties of the larger original graph. This paper introduces both a new method for reducing chain graphs to simpler directed acyclic graphs (DAGs), that we call power chain graphs (PCG), as well as a procedure for structure learning of this new type of graph from correlational data of a Gaussian Graphical model (GGM). A definition for PCGs is given, directly followed by the reduction method. The structure learning procedure is a two-step approach: first, the correlation matrix is used to cluster the variables; and then, the averaged correlation matrix is used to discover the DAGs using the PC-stable algorithm. The results of simulations are provided to illustrate the theoretical proposal, which demonstrate initial evidence for the validity of our procedure to recover the structure of power chain graphs. The paper ends with a discussion regarding suggestions for future studies as well as some practical implications.

Perfect Matchings in Random Octagonal Chain Graphs

A perfect matching of a (molecule) graph G is a set of independent edges covering all vertices in G . In this paper, we establish a simple formula for the expected value of the number of perfect matchings in random octagonal chain graphs and present the asymptotic behavior of the expectation.

An Order-Independent Algorithm for Learning Chain Graphs

LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that PC4LWF is order dependent, in the sense that the output can depend on the order in which the variables are given. This order dependence can be very pronounced in high-dimensional settings. We propose two modifications of the PC4LWF algorithm that remove part or all of this order dependence. Simulation results with different sample sizes, network sizes, and p-values demonstrate the competitive performance of the PC4LWF algorithms in comparison with the LCD algorithm proposed by Ma et al. (2008) in low-dimensional settings and improved performance (with regard to error measures) in high-dimensional settings.