Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients

Bodinier, M., Peronnet, E., Llitjos, JF. et al. Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients. Crit Care 28, 240 (2024). https://doi.org/10.1186/s13054-024-04990-4

Summary of "Integrated Clustering of Multiple Immune Marker Trajectories Reveals Different Immunotypes in Severely Injured Patients"

Introduction

Sepsis and other critical illnesses involve complex immune responses that influence clinical outcomes. Most studies have used single time-point measurements, but the dynamic nature of these responses necessitates longitudinal studies. This study aimed to identify distinct groups of patients based on multiple immune marker trajectories and their association with clinical outcomes.

Materials and Methods

• Patient Cohort: The study analyzed 353 critically ill patients from the REALISM cohort, including those with sepsis, trauma, or major surgery, and 175 healthy volunteers.
• Immune Markers Measurement: Two sets of markers were used: a reference set (REF) of known biomarkers of sepsis immunosuppression and an mRNA set analyzed through multiplexed RT-qPCR.
• Outcomes: Primary outcome was a composite of healthcare-associated infections, death within 30 days, or ICU stay longer than 7 days. Secondary outcomes included 30-day HAI, mortality, and mechanical ventilation-free days.
• Statistical Analysis: Unsupervised clustering was used to define immunotypes based on immune marker trajectories. Clinical variables and outcomes were compared across immunotypes.

Results

• Patient Characteristics: Of 353 patients, 339 were analyzed after excluding those with insufficient data. Most patients had three measurements over the first week.
• Immunotypes Definition: Two immunotypes were identified. Immunotype #1 was associated with poorer outcomes, including higher rates of complicated hospital courses, infections, and mortality, compared to Immunotype #2.
• Biomarker Trajectories: Immunotype #1 showed elevated IL6 and IL10, increased immature neutrophils, and low mHLA-DR levels, indicating a more dysregulated immune response.
• Day 14 Analysis: Immunotype #1 showed sustained immune dysregulation and reduced immune function, while Immunotype #2 exhibited signs of recovery.
• mRNA Set Validation: Similar clustering using mRNA markers confirmed the two immunotypes, with Immunotype #1 again associated with worse outcomes.

Discussion

• Methodology Advantages: The study used an innovative clustering method to capture the dynamic and heterogeneous immune responses in critically ill patients. It identified two immunotypes with distinct clinical outcomes.
• Clinical Implications: Immunotyping could help stratify patients and guide personalized treatment strategies. The mRNA set markers offer a practical advantage for bedside measurement.
• Limitations: The study focused on the first week to minimize missing data. Future research should explore a more comprehensive panel of markers and evaluate single time-point assessments.

Conclusion

The study applied unsupervised clustering to longitudinally profile the immune responses of critically ill patients, identifying two immunotypes with different clinical outcomes. This approach could facilitate patient stratification and inform personalized immunotherapy strategies.

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Discussion Questions

1. How can the identified immunotypes be integrated into clinical practice to guide personalized treatment strategies for critically ill patients?
2. What additional biomarkers or methods could be explored in future studies to enhance the accuracy and clinical utility of immunotyping in critical care?
3. How might single time-point assessments of mRNA markers be used to improve the feasibility of immunotyping in a clinical setting?

Javier Amador-Casta?eda, BHS, RRT, FCCM

Interprofessional Critical Care Network (ICCN)

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