Abstract
Objective:This study sought to establish and validate a dynamic nomogram model for predicting short-term prognosis and identifying the population that benefits from intravenous immunoglobulin (IVIG) therapy in patients with novel bunyavirus sepsis.
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, patients receiving IVIG therapy exhibited significantly improved survival compared to those who did not, with a hazard ratio of 1.756 (95% CI 1.019-3.028) 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.