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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.

Sec. Clinical Infectious Diseases

Volume 15 - 2025 | doi: 10.3389/fcimb.2025.1616538

This article is part of the Research TopicExploring Immunometabolism: Metabolic Pathway and Immune Response in SepsisView all 8 articles

Trajectory of the Systemic Immune-inflammation Index and Inhospital Mortality in Patients with Sepsis

Provisionally accepted
  • The First Hospital of Jilin University, Changchun, Hebei Province, China

The final, formatted version of the article will be published soon.

Background: Sepsis is a complex systemic inflammatory response syndrome triggered by infection with high morbidity and mortality. The systemic immune-inflammation index (SII) is a biomarker of inflammation and immune status. This study investigated the relationship between the SII trajectory and in-hospital mortality in patients with sepsis. Methods: This retrospective study included 1015 adults who were admitted via the emergency department of the First Hospital of Jilin University with a first episode of sepsis between June 2018 and February 2025. Latent-class mixed models (LCMM) were used to identify SII trajectory subgroups, and Cox regression was used to analyze the relationship between subgroups and in-hospital mortality. An eXtreme Gradient Boosting (XGBoost) machine learning model was used to quantify the effect of each variable on the risk of in-hospital mortality. Restricted cubic spline (RCS) analysis assessed the nonlinear relationship between SII and in-hospital mortality. Results: LCMM analysis identified five SII trajectory subgroups. Cox regression analysis showed that Class 1 (the group with continuous increase in SII from a low to medium level), Class 3 (the group with a stable decline in SII from a high level), Class 4 (the group with a stable high SII level) and Class 5 (the group with a stable medium SII level) all had higher risk of in-hospital mortality than Class 2 (the group with a stable medium-high SII level). Class 1 and Class 4 had the highest risk of inhospital mortality (hazard ratio [HR] 15.14 and 6.31, respectively). The XGBoost model confirmed that the SII trajectories were independent predictors of in-hospital mortality. The RCS analysis revealed a U-shaped relationship between the SII within 24 hours after admission and in-hospital mortality. Conclusions: In patients with sepsis, the risk of in-hospital mortality differs according to the SII within 24 hours of admission and the SII trajectory. The risk of in-hospital mortality was greatest in patients whose SII increased continuously and those whose SII stabilized at a high level, and was lowest in patients with an SII stabilized at a medium-high level. The SII within 24 hours after admission had a U-shaped relationship with in-hospital mortality.

Keywords: Sepsis, systemic immune-inflammation index, Restricted cubic spline, predictive model, In-hospital mortality

Received: 23 Apr 2025; Accepted: 01 Sep 2025.

Copyright: © 2025 Xu, Ren, Li and Pang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Li Pang, The First Hospital of Jilin University, Changchun, Hebei Province, China

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