aceClinical phenotyping of bloodstream infections: a review of current evidence
Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effective patient stratification and treatment optimisation. In other heterogeneous conditions such as sepsis, data-driven clinical subphenotyping has identified reproducible subgroups with distinct outcomes and treatment responses. Whether similar approaches can be applied to BSIs to improve clinical management and trial design is an area of growing interest. We aimed to review t…
Unsupervised machine learning applied to bloodstream infections identifies reproducible clinical subphenotypes with different mortality, supporting bedside tools for rapid phenotype assignment and personalized antimicrobial therapy.
Studies use inconsistent phenotyping methods and provide limited validation, slowing translation of AI-derived BSI subphenotypes into routine clinical practice.
Different studies use varying methodological approaches and validation remains limited, with underrepresented BSI aetiologies and lack of integration with biological endotypes.
Evidence
- Peer-reviewedClinical Microbiology and Infection2026-08-26
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Truvace Impact Record TRV-2026-0927, v1: “Clinical phenotyping of bloodstream infections: a review of current evidence.” Truvace, 2026-08-30. /record/TRV-2026-0927 (accessed at citation time). sha256 e1a76c151070f0ed…
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