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Updated: Jul 1, 2026

A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus
Published on: March 28, 2017
Rationalising heterogeneity in Staphylococcus aureus bacteraemia: current progress and future goals
Clark D Russell1,2,3, Sofía De La Villa4,5, Maaike C Swets6
1Centre for Inflammation Research, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Investigating Staphylococcus aureus bacteraemia (SAB) as one disease hinders treatment. Stratifying SAB patients into distinct subphenotypes using data-driven methods is key for better therapies and outcomes.
Area of Science:
- Infectious Diseases
- Clinical Microbiology
- Medical Informatics
Background:
- Staphylococcus aureus bacteraemia (SAB) presents significant clinical and biological heterogeneity.
- Treating SAB as a monolithic entity impedes the development of effective therapeutic strategies.
Purpose of the Study:
- To review and compare recent data-driven patient stratification approaches for SAB.
- To discuss the limitations of current methods and propose future research directions.
- To highlight the importance of addressing heterogeneity for advancing SAB management.
Main Methods:
- Comparison of methodologies from three recent studies (Swets et al, FEN-AUREUS, MRSA-GEIRAS-SEIMC) utilizing routinely available data for SAB patient stratification.
- Analysis of findings related to mortality, microbiologic outcomes, and response to adjunctive therapies.
Main Results:
- Three distinct data-driven approaches identified clinically relevant SAB subphenotypes.
- These subphenotypes exhibit variations in mortality, treatment response, and microbiological outcomes.
- The studies underscore the potential of patient stratification in optimizing SAB management.
Conclusions:
- Addressing clinical and biological heterogeneity in SAB is crucial for transformative advances.
- Progress requires moving from observable clinical subphenotypes to mechanism-based endotypes.
- Future research should focus on refining stratification methods and identifying treatable traits.
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