A Prognostic Model Based on Immune-Inflammatory-Nutritional Indicators for 28-Day Mortality in Sepsis Patients with Acute-on-Chronic Liver Failure.

Background: Sepsis and acute-on-chronic liver failure (ACLF) constitute a severe clinical interaction, resulting in alarmingly high short-term mortality rates. Current prognostic models, such as the Sequential Organ Failure Assessment (SOFA) score, lack specificity for this distinct patient group. The pathophysiology of both conditions is critically influenced by immune dysfunction, systemic inflammation, and nutritional deficits. This study sought to develop and validate a novel prognostic model that integrates immune-inflammatory-nutritional indicators to enhance the prediction of 28-day mortality in sepsis patients with ACLF. Methods: A retrospective cohort study was conducted involving 246 sepsis patients with ACLF admitted to Shaanxi Provincial People's Hospital. Participants were randomly divided into a training cohort (n=185) and a validation cohort (n=61). Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses were utilized to identify independent predictors and construct a prognostic nomogram. The model's efficacy was thoroughly evaluated based on its discriminative capacity (area under the receiver operating characteristic curve, AUC), calibration (calibration curves), and clinical utility (decision curve analysis, DCA). Results: The 28-day mortality rate was observed to be 37.0%. Six independent predictors were identified and integrated into the nomogram: hepatic encephalopathy, D-dimer levels, platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), ALBI grade, and the systemic inflammation response index (SIRI). The nomogram exhibited exceptional discriminative ability, with AUC values of 0.907 and 0.862 in the training and validation cohorts, respectively, significantly surpassing the performance of the SOFA score (AUCs: 0.651 and 0.624). Calibration curves indicated excellent concordance between predicted and observed outcomes, while DCA confirmed a substantial clinical net benefit across a broad range of threshold probabilities. Conclusion: We have successfully developed and validated a robust nomogram that utilizes routinely available immune-inflammatory-nutritional indicators to provide individualized risk estimation of 28-day mortality in sepsis patients with ACLF. This model represents an advanced prognostic instrument when compared to traditional scoring systems, with the potential to enable early detection of high-risk patients and inform individualized therapeutic interventions.