What question did this study set out to answer?

This research aims to improve prediction of sepsis-associated acute kidney injury using machine learning and novel inflammatory biomarkers.

March 14, 2026Open Access

Novel inflammatory biomarkers enhance prediction of sepsis-associated acute kidney injury: a machine learning approach

Key Points

This research aims to improve prediction of sepsis-associated acute kidney injury using machine learning and novel inflammatory biomarkers.
Involved 3613 patients with sepsis and utilized novel inflammatory markers for prediction.
Employed multiple machine learning approaches including logistic regression, random forest, and support vector machine.
Model performance was evaluated using metrics such as F1 score, sensitivity, and area under the ROC curve.
The ensemble model using random forest and extreme gradient boosting achieved an F1 score of 0.843.
XGBoost showed the highest sensitivity at 0.856, identifying patients at risk of S-AKI effectively.
The lactate-to-albumin ratio was the most influential among the novel inflammatory biomarkers.

Abstract

Background: Sepsis-associated acute kidney injury (S-AKI) is a common complication of sepsis, and early identification can improve patient prognosis. This study incorporated novel inflammatory markers as features to construct a model and employed 6 machine learning methods to predict the occurrence of S-AKI. Methods: A total of 3613 patients with sepsis were included in this study. Novel inflammatory markers, including neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic inflammatory response index, systemic immune-inflammation index, systemic inflammatory aggregate index, lactate-to-albumin ratio, and prognostic nutritional index, along with demographic characteristics, clinical conditions, and routine laboratory results, were used to construct the model. The machine learning methods employed included logistic regression, support vector machine, random forest (RF), extreme gradient boosting (XGBoost), and ensemble methods (RF+XGBoost). Model performance and stability were evaluated using 5-fold cross-validation. Model performance was assessed using the area under the receiver-operating characteristic curve, sensitivity, specificity, accuracy, precision, recall, and F1 score. Additionally, SHapley Additive exPlanations values were used to interpret the predictive model. Results: In the final algorithm group, the ensemble model of RF and XGBoost (0.843; 95% confidence interval: 0.820–0.866) was higher than those of other models. Among the single models, the XGBoost model exhibited the highest sensitivity (0.856) and F1 score (0.780), indicating its stronger ability to identify patients who will develop S-AKI, albeit at the expense of lower specificity (0.667). The 4 most influential features for XGBoost were mechanical ventilation, mean arterial pressure, blood urea nitrogen level, and sequential organ failure assessment score. Among the 3 novel inflammatory markers, lactate-to-albumin ratio showed the greatest effect. Conclusion: We successfully developed machine learning methods to predict S-AKI, highlighting the importance of novel inflammatory markers in model construction. This breakthrough offers novel perspectives for feature selection in the future development of related predictive models.

Read Full Paperexternally

Bookmark

View Full Paper

Cite This Study

Zhang et al. (Wed,) studied this question.

synapsesocial.com/papers/69b4ba3618185d8a39802ffd https://doi.org/https://doi.org/10.1097/ec9.0000000000000175

Bookmark

View Full Paper