Machine learning-driven nomogram for predicting viral encephalopathy risk in SFTS patients

Objective To develop a machine learning-driven predictive model for early identification of viral encephalopathy risk in SFTS patients. Materials and methods This retrospective study included 192 SFTS patients (58 with viral encephalopathy) from Nanjing Second Hospital (June 2022–December 2024). Boruta and SHAP-RFE-CV identified nine predictors, refined via LASSO regression (λ.1se=0.018). Multivariate logistic regression analyzed risk factors, constructing a dynamic nomogram. Model performance was validated against APACHE II using AUC, calibration curves, and DCA. PCA explored biomarker interactions. Results Key predictors included viral load, LDH, BNP, IL-8, APTT, and CD4 + T cells. Independent risk factors were LDH (OR=1.18), BNP (OR=3.85), IL-8 (OR=8.97) , prolonged APTT (OR=1.54), while CD4 + T cells were protective (OR=0.55). The nomogram outperformed APACHE II (training AUC=0.958 vs. 0.839; validation AUC=0.974 vs. 0.926). Calibration curves (Hosmer-Lemeshow P=0.48/0.123) and DCA confirmed clinical utility. PCA identified three axes: tissue injury (PC1: LDH/AST, 53.3% variance), inflammation/coagulation (PC2: IL-8/APTT, 13.9%), and immune dysregulation (PC3: CK, 11.6%). IL-8 exhibited a nonlinear threshold effect (cutoff=90.1 pg/mL). Discussion The nomogram integrates dynamic biomarkers to address multicollinearity, outperforming traditional scores. PC1-PC2 axes explained 67.2% of encephalitis variance, highlighting tissue damage and inflammatory-coagulation dysregulation as central mechanisms. Conclusion This study establishes a clinically actionable nomogram for SFTS-associated encephalopathy risk stratification. PCA insights reveal mechanistic interactions, offering novel therapeutic targets.