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Development and external validation of a systemic immune-inflammation index-based prognostic model for 28-day mortality in sepsis-associated acute kidney injury
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DOI：10.1186/s40001-026-05013-4.png)
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Abstract
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This study aimed to evaluate the prognostic value of the systemic immune-inflammation index (SII) in sepsis-associated acute kidney injury (AKI) and to develop a model to predict 28-day mortality. This study used the MIMIC-IV (v2.2) database and included 1,994 patients with sepsis-associated acute kidney injury. An external validation cohort of 486 patients from a single center was also enrolled, with 28-day all-cause mortality as the primary outcome. Variables were selected using LASSO regression, and a prognostic model was developed using multivariable Cox proportional hazards regression. The model was internally validated in the training and testing cohorts and externally validated in an independent clinical cohort. Model performance was assessed using the Akaike information criterion (AIC), concordance index (C-index), and time-dependent AUC, and further evaluated using calibration curves, decision curve analysis (DCA), and Kaplan–Meier survival analysis for discrimination, calibration, and clinical utility. Based on variable selection using LASSO regression and multivariable Cox proportional hazards regression, a baseline Cox prognostic model was developed. Four expanded models were then constructed by incorporating different inflammatory markers. The SII-based model demonstrated relatively better performance in predicting 7- to 28-day survival (C-index = 0.667). The corresponding nomogram showed consistent predictive performance in both the internal and external validation cohorts. Risk stratification analysis indicated that the model successfully stratified patients into high- and low-risk groups, with a statistically significant difference in survival between groups (P < 0.05). The SII-based prognostic model showed consistent predictive performance for survival outcomes in patients with sepsis-associated acute kidney injury across different datasets and may serve as a potential tool for clinical risk stratification and early identification of high-risk patients.
Keywords:
Sepsis
Acute kidney injury
Systemic immune-inflammation index
Inflammation
Mortality
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