{"doi":"10.1101/2025.08.26.25334458","title":"Diagnosing Sepsis Through Proteomic Insights: Findings from a Prospective ICU Cohort","abstract":"Abstract Introduction Sepsis diagnosis remains clinical and heterogeneous. We hypothesized that a proteomics-informed machine-learning approach could identify a small, easy-to-use, and optimized set of clinical variables to complement or potentially outperform SOFA. Methods We conducted a prospective, single-center, observational study in an academic intensive care unit. Plasma from critically ill patients with and without sepsis was analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS). Data were acquired with data-independent acquisition parallel accumulation– serial fragmentation (diaPASEF) and processed using DIA-NN software. Differentially expressed proteins informed model development. Random Forest models were trained in a Discovery cohort (n=55) to select clinical variables linked to the proteome, then tested in an independent Validation cohort (n=59). Recursive feature elimination (RFE) identified a minimal feature set that was predictive of sepsis. The performance was assessed using repeated cross-validation and external validation. Results Twelve plasma proteins differed between sepsis and non-sepsis patients at FDR < 0.1, corresponding to 26 proteome-enriched clinical variables. The classifier achieved mean AUC’s of 0.73 and 0.76 in Discovery and Validation cohorts, respectively. RFE performance plateaued with ≥9 variables, peaked at an accuracy of 0.78, and deteriorated below seven; the final three features before collapse were plasma BUN, chemokine ligand 3 (CCL3), and creatinine. Proteome-to-clinical regression highlighted creatinine as having the strongest correlation (R² = 0.558). Discussion : A concise set of routinely obtainable variables anchored by renal markers and CCL3 captured proteomic signals and discriminated sepsis across cohorts, supporting a “proteomics-informed, clinic-first” strategy for pragmatic EHR deployment. While larger multicenter studies are warranted, these findings suggest that renal dysfunction exerts a disproportionate influence on sepsis and that increased emphasis on kidney-related markers may improve both recognition and risk assessment.","journal":"medRxiv","year":2025,"id":573519,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9159,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1480734,"name":"Shawn J. Rice","orcid":null,"position":1,"is_corresponding":false},{"id":901629,"name":"Anthony Bonavia","orcid":"0000-0001-9596-001X","position":2,"is_corresponding":false},{"id":1338906,"name":"Ahad K. Ardabili","orcid":"0000-0002-0957-1833","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:57:32.445190Z","pmid":"40909819","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}