Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.

Summary

The authors present a goal-directed subgroup identification framework that integrates longitudinal multi-omics to directly optimize sepsis patient stratification for treatment benefit. It predicts survival differences for restrictive versus liberal fluids and ulinastatin, with external validation across critical care databases.

Key Findings

• Introduced a goal-directed subgroup identification framework anchored to treatment-effect optimization using longitudinal multi-omics from 1327 patients across 43 hospitals.
• Stratification by GD-SI benefit scores showed marked survival differences for restrictive versus liberal fluid resuscitation and for ulinastatin immunomodulation.
• External validations in MIMIC-IV and ZiGongDB demonstrated prognostic generalizability and cross-omic concordance.

Clinical Implications

Supports designing precision trials and tailoring fluids or immunomodulation (e.g., ulinastatin) based on benefit scores; could guide early treatment allocation pending prospective validation.

Limitations

• Observational design with potential residual confounding; treatment assignments were not randomized.
• Evaluated therapies (fluid strategy, ulinastatin) may not cover broader intervention classes; real-time clinical implementation needs feasibility testing.

Future Directions

Prospective, randomized trials embedding GD-SI for treatment assignment; expansion to additional interventions and real-time clinical decision support integration.