The Promise of Functional Biomarkers to Guide Critical Care Trial Enrollment-The Promise of Functional Biomarkers to Guide Critical Care Trial Enrollment

Video Transcription

Video Summary

This roundtable, moderated by Dr. Ken Remy, explores the promise of functional biomarkers to guide critical care trial enrollment, particularly in sepsis. Functional biomarkers differ from traditional static markers by dynamically assessing biological responses through ex vivo stimulation, revealing a system’s capacity rather than its current state. Examples include cytokine production assays, thromboelastography, and fluid responsiveness tests. Current use in critical care is promising but limited mostly to prognostic understanding rather than guiding therapy. Challenges include the complexity of critical illness as a dynamic systems problem, unlike single-gene oncological diseases, and difficulties in standardizing assays with rapid turnaround suitable for critically ill patients. The panelists highlighted the potential of functional tests to identify biologically distinct endotypes—subsets of patients with different underlying disease mechanisms—to move beyond syndromic diagnoses like sepsis or ARDS. They envision future adaptive or enrichment trials using these biomarkers for patient stratification, drug response prediction, and real-time therapy adjustment. Collaborative, multidisciplinary research and machine learning integration are essential to translate complex multi-omic data into clinically actionable tools. Ethical concerns regarding cost, access, and equity must be addressed through diverse validation and transparent trial design. Ultimately, the panelists foresee a shift towards personalized, functionally informed critical care, integrating biomarker profiling at ICU admission to tailor treatments and improve outcomes within the next decade.

Keywords

functional biomarkers

critical care

sepsis

cytokine production assays

thromboelastography

fluid responsiveness tests

patient stratification

adaptive clinical trials

machine learning

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