Establishment and validation of a dynamic nomogram to predict short-term prognosis and benefit of human immunoglobulin therapy in patients with novel bunyavirus sepsis in a population analysis study: a multicenter retrospective study.

Abstract

Objective: This study aims to develop a dynamic nomogram model using machine learning to improve short-term prognosis prediction and identify patients who would benefit from intravenous immunoglobulin (IVIG) therapy.

Methods: A multicenter retrospective study was conducted on 396 patients diagnosed with SFTS. Univariate and multivariate Cox regression analyses identified significant predictors of mortality. Machine learning models, including Random Survival Forest, Stepwise Cox Modeling, and Lasso Cox Regression, were compared for their predictive performance. The optimal model, incorporating consciousness, LDH, AST, and age, was used to construct a dynamic nomogram. The nomogram's performance was validated in training, validation, and external test sets. Additionally, the impact of IVIG therapy on survival was assessed within high-risk groups identified by the nomogram.

Results: The dynamic nomogram demonstrated excellent predictive performance with an AUC of 0.903 in the training set, 0.933 in the validation set, and 0.852 in the test set, outperforming SOFA and APACHE II scores. Calibration curves confirmed the model's accuracy. In the high-risk group, the hazard ratio (HR) for death for those who injected immunoglobulin versus those who did not was 0.569 (95% CI 0.330-0.982) in the nomogram model.

Conclusion: The dynamic nomogram effectively predicts short-term prognosis and identifies the population that benefits from IVIG therapy in patients with novel bunyavirus sepsis. This tool can aid clinicians in risk stratification and personalized treatment decisions, potentially improving patient outcomes.