Sepsis subphenotypes, theragnostics and personalized sepsis care

Area of Science:

• Critical care medicine
• Computational biology
• Immunology

Background:

• Patient heterogeneity in sepsis hinders effective therapy development.
• Machine learning and advanced sepsis biology understanding enable subphenotype identification.
• Subphenotyping addresses heterogeneity, potentially revealing treatable patient subgroups.

Purpose of the Study:

• To review emerging sepsis subphenotypes in critically ill patients.
• To outline immune modulation therapies relevant to identified subphenotypes.
• To discuss integrating subphenotype identification into precision medicine clinical trials.

Main Methods:

• Review of current literature on sepsis subphenotypes.
• Analysis of machine learning applications in sepsis research.
• Examination of clinical trial data re-analyses.

Main Results:

• Machine learning has identified distinct patient subphenotypes in critical illness.
• Different subphenotypes show varied responses to existing treatments.
• Precision medicine approaches in trials can improve therapeutic discovery.

Conclusions:

• Subphenotype identification is crucial for advancing sepsis treatment.
• Targeted therapies based on subphenotypes offer a path to precision medicine.
• Bedside implementation of subphenotyping can revolutionize clinical trials.