ArticleInfection2025
Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.
Article in Infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Meeting the future at the Munich conference ESCMID-Global 2026.Infection · 2026Article
- Human Cytomegalovirus Serostatus Defines Cytokine-Based Predictive Signatures in Sepsis.Pathogens (Basel, Switzerland) · 2026Observational
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Identification of clinical phenotypes and prediction model for the mixed-infection phenotype of pediatric community-acquired pneumonia based on unsupervised machine learning.Frontiers in pediatrics · 2026Article
- Immune cell profiling supports early prediction of sepsis-associated acute kidney disease using a decision tree algorithm.Biomarker research · 2025Article
- Liquid-liquid phase separation-related genes in the immune microenvironment and diagnosis of focal segmental glomerulosclerosis and minimal change disease.Renal failure · 2025Article
- Plasma proteomics identifies molecular subtypes in sepsis.Critical care (London, England) · 2025Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
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Authors and funding
23 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis therapy is still limited to treatment of the underlying infection and supportive measures. To date, various sepsis subtypes were proposed, but therapeutic options addressing the molecular changes of sepsis were not identified. With the aim of a future individualized therapy, we used machine learning (ML) to identify clinical phenotypes and their temporal development in a prospective, multicenter sepsis cohort and characterized them using plasma proteomics.
methodsRoutine clinical data and blood samples were collected from 384 patients. Sepsis phenotypes were identified based on clinical measurements and plasma samples from 301 patients were analyzed using mass spectrometry. The obtained data were evaluated in relation to the phenotypes, and supervised ML models were developed enabling prospective phenotype classification.
resultsThree sepsis phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure. Cluster B showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. Time course analysis showed a strong association of cluster C with mortality, while patients in cluster B were likely to change the cluster until day 4. The plasma proteome reflected the clinical features of the phenotypes and showed gradual consumption of complement and coagulation factors with increasing sepsis severity. Supervised ML models allowed the assignment of patients based on only seven widely available features (alanine transaminase (ALT), aspartate transaminase (AST), base excess (BE), international normalized ratio of thrombin time (INR), diastolic arterial blood pressure, systolic arterial blood pressure (BPdia, BPsys) and activated partial thromboplastin time (aPTT)).
conclusionsThe identified clinical phenotypes reflected varying degrees of sepsis severity and were mirrored in the plasma proteome. Proteomic profiling offered novel insights into the molecular mechanisms underlying sepsis and enabled a deeper characterization of the identified phenotypes.
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