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Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses
Published on: August 30, 2018
Structural Equation Modelling as a Proof-of-Concept Tool for Mediation Mechanisms Between Topical Antibiotic
James Hurley1,2,3
1Melbourne Medical School, University of Melbourne, Parkville, VIC 3052, Australia.
Abstract:
Whether exposing the microbiome to antibiotics decreases or increases the risk of blood stream infection with Pseudomonas aeruginosa, Staphylococcus aureus, Acinetobacter, and Candida among ICU patients, and how this altered risk might be mediated, are critical research questions. Addressing these questions through the direct study of specific constituents within the microbiome would be difficult. An alternative tool for addressing these research questions is structural equation modelling (SEM). SEM enables competing theoretical causation networks to be tested 'en bloc' by confrontation with data derived from the literature. These causation models have three conceptual steps: exposure to specific antimicrobials are the key drivers, clinically relevant infection end points are the measurable observables, and the activity of key microbiome constituents on microbial invasion serve as mediators. These mediators, whether serving to promote, to impede, or neither, are typically unobservable and appear as latent variables in each model. SEM methods enable comparisons through confronting the three competing models, each versus clinically derived data with the various exposures, such as topical or parenteral antibiotic prophylaxis, factorized in each model. Candida colonization, represented as a latent variable, and concurrency are consistent promoters of all types of blood stream infection, and emerge as harmful mediators.
Insights
Antibiotic exposure in intensive care units (ICUs) can alter the risk of bloodstream infections from common pathogens. Structural equation modeling reveals that Candida colonization and concurrent infections act as harmful mediators, increasing infection risk.
Area of Science:
- Microbiology
- Medical Informatics
- Epidemiology
Background:
- Antibiotic use in intensive care units (ICUs) can disrupt the microbiome, potentially altering the risk of bloodstream infections (BSIs).
- Investigating the complex interplay between antibiotic exposure, microbiome changes, and specific pathogens like *Pseudomonas aeruginosa*, *Staphylococcus aureus*, and *Acinetobacter*, as well as *Candida*, is challenging through direct microbial studies.
- Understanding the mediating factors in these altered infection risks is crucial for patient care.
Purpose of the Study:
- To investigate the impact of antibiotic exposure on the risk of BSIs caused by specific pathogens in ICU patients.
- To explore the mediating mechanisms through which microbiome alterations influence BSI risk.
- To compare different theoretical causation models of BSI risk using structural equation modeling (SEM).
Main Methods:
- Employed structural equation modeling (SEM) to test competing theoretical causation networks.
- Utilized literature-derived data to confront causation models with clinically relevant infection endpoints.
- Modeled antibiotic exposure (e.g., topical or parenteral prophylaxis) as key drivers and unobservable microbiome constituents as latent variable mediators.
Main Results:
- SEM analysis allowed for the testing of multiple causation models against clinical data.
- *Candida* colonization, treated as a latent variable, consistently emerged as a significant promoter of all types of BSIs.
- Concurrent infections were also identified as harmful mediators that promote BSIs.
Conclusions:
- Structural equation modeling provides a viable approach to study complex relationships between antibiotic exposure, microbiome, and BSI risk.
- *Candida* colonization and concurrent infections are identified as critical harmful mediators in the development of BSIs in ICU patients.
- The findings highlight the importance of considering fungal colonization and co-infections when assessing BSI risk in antibiotic-exposed ICU populations.
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