Clinic-first sepsis recognition in the ICU: a proteomics-guided, parsimonious model with independent validation

Sepsis recognition in the ICU remains variable and relies on consensus clinical criteria rather than biomarker-defined rules. Routine laboratory and physiologic data often overlap with noninfectious critical illness, obscuring early identification. We evaluated whether discovery proteomics could prioritize a concise set of routinely obtainable clinical variables, yielding a practical, clinic-first model that distinguishes sepsis from other critical illness. In a prospective, single-center pilot at an academic medical center, we enrolled adults within 48 h of critical illness onset (sepsis and non-sepsis comparators). Plasma proteomics by LC-MS/MS with diaPASEF identified proteins differentiating groups and guided selection of proteome-enriched routine variables for modeling. A Random Forest classifier was trained in a Discovery cohort (n = 55) and evaluated in an independent Validation cohort (n = 59), with prespecified attention to discrimination, parsimony, and feasibility for electronic health record (EHR) deployment. Twelve plasma proteins differed between groups at FDR < 0.10, supporting biological separation. A parsimonious model using routine predictors ± CCL3 achieved AUC 0.73 in Discovery and AUC 0.76 in the independent Validation cohort. Recursive feature elimination demonstrated a parsimony plateau at ~ 9 variables; beyond this threshold, further reduction degraded accuracy. Notably, blood urea nitrogen, CCL3 (measured by multiplex immunoassay), and creatinine were the final features retained before performance declined, aligning with renal stress and inflammatory signaling. Figures present ROC curves and the parsimony profile, highlighting a minimal variable set compatible with typical ICU workflows and decision-support systems. A proteomics-informed, clinic-first strategy produced a parsimonious set of routine variables that discriminated sepsis from other ICU critical illness with clinically meaningful accuracy and an immediately actionable footprint. Because most predictors are routinely captured in the EHR, the model is EHR-compatible; CCL3 is readily measurable on standard immunoassay platforms if adopted locally. These findings justify multicenter studies to confirm generalizability and calibration, evaluate real-time integration into ICU workflows, and test whether an early recognition adjunct improves timeliness of sepsis care and patient outcomes.