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September 5, 2025Open Access

Diagnosing Sepsis Through Proteomic Insights: Findings from a Prospective ICU Cohort

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Observational study revealed that a proteomics-based approach improves sepsis diagnosis, highlighting key clinical variables.

A concise set of clinical variables linked to proteomics achieved a mean AUC of 0.76 for sepsis diagnosis.
Differentially expressed proteins identified twelve markers that distinguished sepsis patients from others at FDR<0.1.
Random Forest models used recursive feature elimination to find nine predictive clinical variables for sepsis.
The study emphasizes the potential of a 'proteomics informed, clinic first' approach for electronic health records.

Ardabili et al. (2025) studied this question.

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