Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease with high mortality, and clinical practice lacks dynamic tools to assess its rapidly evolving course. This study aims to develop stage-specific machine learning models to predict mortality risk using longitudinal biomarker data. We conducted a retrospective analysis of 5359 laboratory-confirmed SFTS patients from two hospitals in the highly endemic region in China. Serial measurements of 46 clinical and laboratory variables were integrated into a three-stage prognostic model developed using extreme gradient boosting (XGBoost). Within each clinical stage, key predictors and their relative contribution (RC) of mortality risk were assessed. Model performance was assessed based on discrimination, calibration, and decision curve analysis (DCA) in internal and external test sets. XGBoost models were constructed across 10 temporal phases, later consolidated into three clinically distinct stages via hierarchical clustering: early (≤7 days), intermediate (days 8-9), and late (≥10 days). Key predictors included age (dominant in early phase; RC, 18.44%), lactate dehydrogenase (LDH; RC peaking at 60.10% in late phase), and monocyte percentage (RC range from 5.25% to 16.04%). Pathophysiological shifts across clinical stages were revealed: early viral cytopathy (dominated by age and MONO%), intermediate immunopathology (marked by LDH surge), and late hepatic failure (dominated by LDH, AST, and TBA). The model showed strong discrimination (Area under the receiver operating characteristic curve, AUCs: 0.84-0.98 internal; 0.91-0.98 external), calibration (Brier scores: 0.04-0.11), and clinical utility via DCA. This study introduces a dynamic staging system that leverages predictive models and real-time patient data to monitor mortality risk and personalize SFTS care, which enables timely interventions to reduce deaths.