Identification of Clinical Subphenotypes in Sepsis via Mixed Data Cluster Analysis

Overview

This multicenter observational study identified distinct sepsis subphenotypes using mixed clinical data and demonstrated variability in treatment responses across these groups. The findings suggest that tailored adjunctive therapies may improve outcomes in specific sepsis subphenotypes.

Background

Sepsis remains a leading cause of mortality worldwide despite guideline-recommended treatments such as fluid resuscitation, antimicrobials, and vasopressors. Previous studies have treated sepsis as a homogeneous condition, potentially obscuring treatment effects due to patient heterogeneity. Cluster analyses using only continuous variables have been performed, but mixed data clustering incorporating categorical and continuous variables may better capture clinical complexity. Furthermore, prior research has not adequately assessed differential treatment responses across multiple adjunctive therapies in sepsis subphenotypes.

Data Highlights

The study analyzed 52 clinical variables including demographics, vital signs, organ function markers, and coagulation parameters from ICU patients with severe sepsis or septic shock. Data were derived from two large Japanese multicenter registries (Tohoku Sepsis Registry, FORECAST) for derivation and two additional registries (JAAM SPICE-ICU, JAAM MAESTRO) for validation. Six adjunctive therapies with weak guideline recommendations were evaluated for treatment effect variability among subphenotypes.

Key Findings

• Cluster analysis using mixed data (categorical and continuous variables) identified distinct sepsis subphenotypes with differing clinical characteristics.
• Subphenotypes showed significant differences in in-hospital mortality rates, indicating prognostic relevance.
• Variability in treatment responses to adjunctive therapies such as corticosteroids, polymyxin B hemoperfusion, recombinant thrombomodulin, antithrombin III, immunoglobulin G, and vasopressin was observed across subphenotypes.
• Some adjunctive therapies demonstrated potential benefit in specific subphenotypes but not others, highlighting heterogeneity in treatment effectiveness.
• Use of multiple treatments was analyzed simultaneously, reflecting real-world clinical practice more accurately than prior single-treatment studies.

Clinical Implications

Recognizing sepsis subphenotypes through mixed data cluster analysis can guide personalized treatment strategies, potentially improving patient outcomes by targeting therapies to those most likely to benefit. Clinicians should consider patient heterogeneity when selecting adjunctive therapies, moving beyond a one-size-fits-all approach. Incorporation of such subphenotyping into clinical decision-making may advance precision medicine in sepsis care.

Conclusion

This study demonstrates that mixed data cluster analysis can identify clinically relevant sepsis subphenotypes with distinct treatment response profiles. Tailoring adjunctive therapies based on subphenotype classification holds promise for enhancing sepsis management and outcomes.

References

1. Sepsis Guidelines and Adjunctive Therapies References [1-14]
2. Cluster Analysis and Subphenotyping in Sepsis [15-26]
3. Registry Studies: Tohoku Sepsis Registry and FORECAST [29,30]
4. Validation Registries: JAAM SPICE-ICU and JAAM MAESTRO [31,32]
5. SOFA Score Reference [33]