Construction and validation of a dynamic nomogram using Lasso-logistic regression for predicting the severity of severe fever with thrombocytopenia syndrome patients at admission

Abstract

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression. Methods All eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model’s performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance. Results 299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017–1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003–1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175–2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005–1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035–26.858; P < 0.001) and hemorrhage symptoms (OR, 3.414; 95% CI, 1.096–10.974; P = 0.035). The AUC-ROC, C-index, calibration plots, and DCA demonstrated the robust performance of the nomogram in predicting severity at admission, and Kaplan-Meier survival analysis indicated its utility in predicting 28-day mortality among SFTS patients. The dynamic nomogram is accessible at https://sfts.shinyapps.io/SFTS_severity_nomogram/ . Conclusion This study provided a practical and readily applicable tool for the early identification of high-risk SFTS patients, enabling the timely initiation of intensified treatments and protocol adjustments to mitigate disease progression.

Keywords

• Severe fever with thrombocytopenia syndrome
• Prediction model
• Nomogram
• Severe status