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

Area of Science:

• Critical Care Medicine
• Immunology
• Computational Biology

Background:

• Sepsis heterogeneity hinders immunotherapy progress.
• Artificial intelligence (AI) and machine learning (ML) offer novel approaches to analyze complex sepsis data.
• Understanding sepsis subphenotypes is crucial for targeted treatment strategies.

Purpose of the Study:

• To review existing evidence on AI-derived sepsis subtyping.
• To explore treatment responses to immune modulating therapies based on sepsis subphenotypes.
• To discuss the implications of AI in sepsis research.

Main Methods:

• A structured literature search was performed on MEDLINE.
• A narrative review of relevant publications was conducted.
• The review focused on studies utilizing clinical, biological, and omics data for sepsis subtyping.

Main Results:

• Multiple AI/ML algorithms for sepsis subtyping were identified.
• The primary classification distinguished between hyper- and hypoinflammatory subphenotypes.
• Statins, corticosteroids, activated protein C, and thrombomodulin showed differential effects across sepsis subphenotypes.

Conclusions:

• Prospective validation of AI-derived sepsis subtypes is essential.
• AI-driven subtyping holds potential for personalized immunotherapy in sepsis.
• Addressing validity, equity, ethics, and feasibility is critical for clinical implementation.