What type of study is this?

This is a Randomized Controlled Trial study.

What question did this study set out to answer?

This work explores AI and machine learning applications in sepsis subphenotyping for personalized immunotherapy.

January 6, 2026Open Access

Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis

EKEvdoxia KyriazopoulouNational and Kapodistrian University of Athens EKEleni KarakikeNational and Kapodistrian University of Athens PMPavlos MyrianthefsNational and Kapodistrian University of Athens

Key Points

This work explores AI and machine learning applications in sepsis subphenotyping for personalized immunotherapy.
Conducted a structured search on MEDLINE for relevant publications on 1 October 2025.
Performed a narrative review of identified studies.
Analyzed various subphenotyping algorithms using clinical, biological, and omics data.
Identified multiple algorithms classifying patients into hyper- and hypoinflammatory subphenotypes.
Differential treatment response observed with therapies like statins and corticosteroids.
Highlighted the need for further research to validate clinical findings on sepsis treatment.

Abstract

Background/Objectives: Sepsis heterogeneity limits advances in immunotherapy. Increasing use of artificial intelligence (AI) and machine learning (ML) attempts to turn multi-dimensional data into meaningful clusters, indicating biological mechanisms. We provide an overview of the existing evidence on AI-derived sepsis subtyping, exploring treatment response to available immune modulating therapies. Methods: On 1 October 2025, we conducted a structured search on all relative publications on MEDLINE and undertook a narrative review. Results: Multiple subphenotyping algorithms were identified, using clinical, biological, and omics data, across different cohorts, mainly through secondary analyses of randomized trials. The main classification was between hyper- and hypoinflammatory subphenotypes. Statins, corticosteroids, activated protein C, or thrombomodulin displayed differential effects on the outcome of these subphenotypes. Conclusions: Further research is required to prospectively validate findings and to offer pragmatic solutions to patients who need them the most. Issues of validity, equity, ethics, and feasibility are discussed.

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