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Association between blood urea nitrogen trajectory phenotypes and prognosis in patients with sepsis: a multicenter retrospective cohort study

Year: 2026

Type: article

Abstract: BACKGROUND: Sepsis exhibits substantial heterogeneity, and its stratification may facilitate precision medicine. Whether blood urea nitrogen (BUN) trajectories can be used for phenotyping sepsis patients remains unclear. METHODS: Sepsis patients meeting the criteria were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database (eICU-CRD). Latent class trajectory modeling (LCTM) was used to identify BUN trajectory phenotypes during the first 7 days. Multivariable logistic regression was used to analyze the association between trajectory phenotypes and prognosis. RESULTS: A total of 13,319 sepsis patients were included from the MIMIC-IV database, and 12,560 from the eICU-CRD database. LCTM identified five distinct BUN trajectory phenotypes. Compared with Trajectory Type 1 (persistently low level), other types were significantly associated with increased 28-day mortality. Among them, Type 4 patients in the MIMIC-IV cohort (decrease followed by increase) had the highest odds ratio (OR) of 2.28 (95% Confidence Interval [CI] 1.74–3.98), P < 0.001; similarly Trajectory Type 4 in the eICU-CRD cohort also had the highest OR of 4.71 (95%CI 1.69–6.01), P < 0.001. Results of survival analysis and subgroup analysis were consistent. CONCLUSION: BUN trajectory phenotypes can effectively identify sepsis patients with different prognoses, among whom patients with BUN that decreases then increases or remains persistently high have the highest mortality risk. BUN trajectory phenotypes may provide a simple and effective risk stratification tool for sepsis. CLINICAL TRIAL NUMBER : Not applicable.

Source: BMC Infectious Diseases

DOI: 10.1186/s12879-026-13339-x

Authors: Ying Jiang, Huafeng Ding, Xiangquan Li

Institutions: Xuzhou Central Hospital, Southeast University

Language: English

Topic: Sepsis Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,090