Abstract / Summary
Abstract Sepsis is a life-threatening syndrome characterized by substantial clinical heterogeneity. The urea-to-creatinine ratio (UCR) is a readily available biomarker reflecting systemic metabolic stress and renal handling; however, the prognostic value of its dynamic trajectories in sepsis remains unclear. This study aimed to identify UCR trajectory phenotypes in patients with sepsis using longitudinal data analysis and to investigate their associations with 30-day all-cause mortality. This retrospective cohort study was conducted using data from the MIMIC-IV database. A total of 7145 patients who met the Sepsis-3 diagnostic criteria were included. Longitudinal UCR measurements within the first 7 days after ICU admission were analyzed using latent class growth mixture modeling (LCGMM) to identify distinct trajectory patterns. Kaplan–Meier survival analysis and multivariable Cox proportional hazards regression were performed to evaluate the associations between UCR trajectory phenotypes and 30-day all-cause mortality. Five distinct UCR trajectory classes were identified by LCGMM: Class 1 (low-to-high sharp increase, 2.9%), Class 2 (low-to-moderate gradual increase, 24.3%), Class 3 (persistently low stable, 55.6%), Class 4 (high-to-low rapid decline, 2.0%), and Class 5 (moderately high gradual decline, 15.1%). Using Class 1 as the reference, multivariable Cox regression analysis demonstrated significantly lower mortality risks in all other classes (hazard ratios [HRs]: Class 2: 0.469; Class 3: 0.346; Class 4: 0.251; Class 5: 0.350; all P < 0.001). Subgroup analyses further revealed that the prognostic value of UCR trajectories was more pronounced in patients without acute kidney injury (AKI) and in those not requiring mechanical ventilation (interaction P < 0.001). LCGMM-based modeling of dynamic UCR trajectories effectively identifies sepsis phenotypes with distinct prognoses. Trajectories characterized by a sharp increase or persistently elevated UCR are associated with the highest mortality risk. This approach demonstrates enhanced risk stratification capability, particularly among patients without documented AKI. Our findings provide novel evidence supporting the use of routinely available, dynamic laboratory indicators for prognostic assessment and individualized management in patients with sepsis.