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. 2013 Apr;51(4):1237-40.
doi: 10.1128/JCM.02157-12. Epub 2013 Jan 9.

Molecular epidemiological surveillance of multidrug-resistant Pseudomonas aeruginosa isolates in a pediatric population of patients with cystic fibrosis and determination of risk factors for infection with the Houston-1 strain

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Molecular epidemiological surveillance of multidrug-resistant Pseudomonas aeruginosa isolates in a pediatric population of patients with cystic fibrosis and determination of risk factors for infection with the Houston-1 strain

Ruth Ann Luna et al. J Clin Microbiol. 2013 Apr.

Abstract

Multiyear molecular epidemiological surveillance of multidrug-resistant Pseudomonas aeruginosa (MRPA) in a pediatric cystic fibrosis care center identified an endemic MRPA strain (Houston-1). Recent hospitalization was found to be a statistically significant risk factor for acquisition of the endemic strain. Multiple infection control improvements led to the reduced incidence of the Houston-1 strain in the CF population.

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Figures

Fig 1
Fig 1
The endemic Houston-1 strain depicted by a dendrogram. The dendrogram is a hierarchical tree representation that uses group averages to display relative similarities or clusters of the isolates. Each patient in the report data set is assigned a key number by the online report generator, and the “Pt ID” column represents the anonymous identifier assigned to each patient for the study. The vertical line delineates the 95% similarity cutoff, and the box identifies the Houston-1 strain. Isolates with a molecular profile that is ≥95% similar to all other isolates within a group indicate a cluster of interest. The Houston-1 group is the largest and most significant cluster identified in the complete data set.
Fig 2
Fig 2
Scatterplot representation of MRPA clones from patients with CF. The scatterplot is a nonhierarchical data display that uses a two-dimensional spatial representation to display relative similarities of isolates by virtue of relative distances on a grid. The Houston-1 strain is highlighted by the circle in the center of the scatterplot grouping. This graphical view provides a two-dimensional interpretation of groups of related isolates, allowing for clusters and their resulting outliers to be easily detected.

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