Predicting mortality risk of severe fever with thrombocytopenia syndrome: A multi-center retrospective cohort study.

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,

SUBMITTER: Hong XG 

PROVIDER: S-EPMC12826966 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature