Unveiling the immuno-inflammatory endotype of sepsis-associated acute kidney injury: a machine learning-based approach.

BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and high-mortality complication in critically ill patients. Current diagnostic criteria rely on functional markers that often lag behind the onset of renal injury. While immune dysregulation is central to SA-AKI pathogenesis, few prediction models systematically integrate multidimensional immune phenotypes. METHODS: The present analysis was based on a multicenter prospective cohort study involving 1,551 septic patients. The study was carried out across five tertiary hospitals in Beijing. Clinical data and immuno-inflammatory biomarkers (including humoral, complement, and T lymphocyte subsets) were collected within 24 hours of sepsis diagnosis. An Extreme Gradient Boosting (XGBoost) model was then developed to predict SA-AKI. RESULTS: New-onset SA-AKI occurred in 44.8% of the cohort and was associated with increased mortality. We identified a distinct high-risk immuno-inflammatory endotype characterized by elevated immunoglobulin A and procalcitonin (PCT), consumptive complement component 3 depletion, and a "complex and divergent cellular immune pattern"-manifested as depleted naïve CD4+ T cells coexisting with expanded CD28+CD4+ T cells. Integrating these immune features with routine clinical variables (Acute Physiology and Chronic Health Evaluation II (APACHE II), the Sequential Organ Failure Assessment [SOFA], PCT, prothrombin time [PT], sex) yielded an XGBoost-based clinical-immune model with superior discrimination (area under the curve [AUC], 0.914), significantly outperforming the clinical model (AUC, 0.788). Decision curve analysis further confirmed greater net clinical benefit for the integrated model. CONCLUSION: Immune dysregulation plays an important role in developing AKI in sepsis patients. Immune biomarkers could significantly improve early identification of high SA-AKI risk patients.