A Combined Inflammatory-Nutritional Index Model for Early Prognosis Prediction in Sepsis: Evidence From Logistic...

Sepsis remains a challenging condition with high short-term mortality despite advances in intensive care, emphasizing the significance of early prognostic assessment in guiding treatment decisions. Inflammatory and nutritional-immune indices are well-established predictors of clinical outcomes in critically ill patients. This study aims to evaluate the prognostic significance of the systemic immune-inflammation index (SII) and the Naples Prognostic Score (NPS) in predicting 28-day survival outcomes among individuals diagnosed with sepsis. The study also aims to construct and assess a visual predictive tool-specifically, a nomogram-that incorporates both of these biomarkers.

This retrospective study analyzed clinical data recorded from patients with sepsis who were treated in the intensive care unit (ICU) of The People's Hospital of Cangnan between January 2021 and December 2023. Applying pre-determined inclusion-exclusion criteria, 324 cases were ultimately included in the final analysis. Comprehensive baseline data, including clinical features and laboratory findings, were systematically retrieved from the electronic health record system. Mortality-associated markers were identified within 28 days using univariate analyses followed by multivariable logistic regression, with SII included as a continuous variable and NPS as a categorical variable. The predictive performance of SII and NPS, both individual and in combination, was assessed using receiver operating characteristic (ROC) curve. A predictive nomogram was developed, and the accuracy and clinical utility of the model were then evaluated using calibration plots and decision curve analysis (DCA).

The analysis revealed that higher SII values (p < 0.001) and NPS scores (≥2, p < 0.001) were closely associated with increased 28-day mortality. Multivariate logistic regression analysis identified SII (p < 0.001) and NPS (≥2, p < 0.001) as independent risk predictors. A predictive model was developed by combining both SII and NPS, demonstrating a superior area under the ROC curve [area under the curve (AUC): 0.846] compared to models utilizing either indicator alone. Furthermore, the nomogram that incorporated these two parameters exhibited high consistency between predicted probabilities and actual outcomes, while also demonstrating strong net clinical benefit in DCA.

SII and NPS are robust and independent predictors of short-term mortality in sepsis. The nomogram developed from these indicators offers a practical, data-driven approach to individualized risk prediction. This study highlights the clinical utility of integrating inflammatory and nutritional-immune indices in prognostic evaluation.