Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration

Area of Science:

• Biomedical research
• Critical care medicine
• Genomics and proteomics

Background:

• Fluid management in septic shock lacks individualized strategies.
• Current approaches do not adequately stratify patient responses to fluid therapy.

Purpose of the Study:

• To develop a statistical model using transcriptomics to identify septic shock patient subgroups with differential fluid strategy responses.
• To create a proteomic signature for predicting fluid strategy benefit.

Main Methods:

• Analysis of transcriptomic data from 494 septic shock patients.
• Derivation of a benefit score from transcriptome space.
• Development of a six-protein proteomic signature for benefit score prediction.

Main Results:

• Adherence to the recommended fluid strategy showed a hazard ratio of 0.82 (95% CI: 0.64-0.92).
• Potential reduction in hospital mortality from 16% to 13% with adherence.
• Proteomic signature achieved an AUC of 0.802 (95% CI: 0.752-0.846) for predicting benefit.

Conclusions:

• A transcriptomics-based statistical model can identify patient subgroups for tailored fluid strategies.
• A novel proteomic signature shows promise in guiding fluid management for septic shock.
• Personalized fluid strategies informed by molecular signatures may improve septic shock patient outcomes.