Background: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication of sepsis. While the static lactate-to-albumin ratio (LAR) has prognostic value, its dynamic temporal evolution during early resuscitation and its utility for guiding clinical risk stratification remain underexplored. Objective: The aim of the study is to identify distinct dynamic trajectories of LAR, evaluate their independent associations with adverse clinical outcomes, and construct a practical prognostic nomogram for patients with SA-AKI. Methods: Data were extracted from the Medical Information Mart for Intensive Care IV database, including adult patients with SA-AKI and ≥ 3 lactate and albumin measurements within the first 72 hours of intensive care unit admission. A multicenter external validation cohort was assembled from 5 tertiary hospitals in Beijing, China. Group-based trajectory modeling identified distinct LAR trajectories. The primary outcome was 28-day mortality; secondary outcomes included 90-day mortality and continuous renal replacement therapy initiation. Trajectory-outcome associations were assessed using multivariable Cox and logistic regression models. Robustness was examined using restricted cubic splines, inverse probability of treatment weighting, weight truncation, doubly robust estimation, and subgroup analyses. Incremental predictive value beyond single baseline LAR was quantified, and a trajectory-integrated nomogram was developed and externally validated. Results: Among the 615 patients in the primary cohort, 3 LAR trajectories were identified: trajectory 1 (low-stable type), trajectory 2 (rapid-clearance type), and trajectory 3 (delayed-clearance type). In adjusted multivariable models, trajectory 3 (vs trajectory 1) was independently associated with increased 28-day mortality risk (hazard ratio 1.63, 95% CI 1.00‐2.64; P =.0496), 90-day mortality (hazard ratio 1.72, 95% CI 1.14‐2.60; P =.01), and continuous renal replacement therapy initiation (odds ratio 3.40, 95% CI 1.64‐7.15; P =.001). Conversely, the mortality risk for trajectory 2 did not differ significantly from trajectory 1. Sensitivity analyses supported these findings, although trajectory 3 associations attenuated in inverse probability of treatment weighting–weighted analyses. Incorporating trajectories improved predictive accuracy over single baseline LAR (continuous net reclassification improvement 0.170, integrated discrimination improvement 0.023; both P =.01). In the external validation cohort (n=508), 3 analogous trajectories were identified. Trajectory 3 consistently conferred a higher mortality risk across both cohorts, whereas trajectory 2 was significantly associated with 28-day mortality only in validation. The nomogram exhibited modest discrimination in the external cohort (concordance index=0.612), acceptable calibration, and potential utility as a supplementary bedside risk stratification tool. Conclusions: Dynamic LAR trajectories are independently associated with 28- and 90-day mortality in SA-AKI. The delayed-clearance trajectory identifies a specific high-risk phenotype, suggesting that longitudinal LAR monitoring and the constructed nomogram may support early risk stratification and inform clinical decision-making.

Purpose: In this study, various informatics analyses were employed to identify the hub genes associated with septic cardiomyopathy (SCM) onset and investigate their immune infiltration status. Methods: High‐throughput sequencing data of myocardial tissue samples from mice with SCM were obtained from the GEO database and our previously published articles. The Limma and weighted gene co‐expression network analysis (WGCNA) packages were used to identify the hub genes associated with SCM onset. GSEA and the DAVID database were employed for gene enrichment analysis. Additionally, the CIBERSORT database was used to analyze the immune infiltration in SCM. Finally, the multiMiR package was used to analyze the microRNAs acting as ceRNAs for the hub genes. Receiver operating characteristic (ROC) curves and Mendelian randomization analysis were used to evaluate the predictive value of hub genes for SCM. Results: The SCM group included nine samples, while the control group included ten samples. SCM upregulated 15 genes and downregulated 7. Mt1 and Actc1 were the most significantly upregulated and downregulated, respectively. GO analysis indicated that the most significantly enriched biological process was “response to bacterium,” and the most enriched signaling pathway was “mineral absorption.” Immunoinfiltration analysis revealed decreased T cells CD4 naive, B cells naive, resting mast cells, and M2 macrophage infiltration in the hearts of SCM mice. WGCNA and Limma package analyses identified Clu , Igf1 , and Trp53 as hub genes associated with SCM onset. The ROC curves demonstrated a strong correlation and predictive value for Trp53 , Igf1 , and Clu in the SCM. Moreover, Clu and Igf1 demonstrated predictive values for SCM using Mendelian randomization analysis from the IEU database. Eleven miRNAs formed a ceRNA network with these hub genes. Conclusion: In summary, our results implicated Igf1 and Clu as the potential candidates involved in SCM pathogenesis.

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. 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. 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. 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.