Development and validation of an early risk-stratification model for hemophagocytic lymphohistiocytosis in severe fever with thrombocytopenia syndrome.

objectiveTo develop and validate a model for early risk stratification of secondary hemophagocytic lymphohistiocytosis (HLH) in patients with severe fever with thrombocytopenia syndrome (SFTS).

methodsThis retrospective cohort included adults with laboratory-confirmed SFTS admitted to The First Affiliated Hospital with Nanjing Medical University between January 2019 and July 2024. Predictor variables were derived from clinical and laboratory data obtained within 3 days after virologic confirmation, corresponding to the predefined early-evaluation window of the study. HLH status (binary outcome) was defined using the entire-course HScore (≥170), calculated from the worst available values over the clinical course; HLH-2004 criteria and early-window HScore distributions were summarized descriptively to provide transparent outcome accounting. Twenty-eight candidate predictors entered LASSO with Boruta refinement, and retained variables were used to construct a multivariable logistic model and nomogram. Model performance was evaluated by discrimination, calibration, and decision-curve analysis in the derivation cohort, an internal validation set, and an external cohort of 60 patients from The First Affiliated Hospital with Anhui Medical University.

resultsAmong 249 patients (152 non-HLH, 97 HLH), HLH was associated with higher peak temperature, longer fever, more lymphadenopathy/splenomegaly and neurological symptoms, and more severe thrombocytopenia, hypertriglyceridemia, hypofibrinogenemia, hyperferritinemia, higher viral load, elevated muscle/liver enzymes and LDH, and coagulopathy (all p < 0.05). LASSO-Boruta identified six routinely available predictors-peak temperature, splenomegaly, fever duration, triglycerides, fibrinogen, and ferritin. The model showed LR χ² = 214.82 (p < 0.0001), R² = 0.784, C-index = 0.962, Dxy = 0.923, with near-perfect calibration in derivation. In internal validation, discrimination remained near-perfect (AUC 0.997, 95% CI 0.989-1.000); mild miscalibration was corrected by intercept-and-slope recalibration, and decision curves showed net benefit across wide thresholds. External validation (n = 60) confirmed excellent discrimination (AUC 0.907, 95% CI 0.835-0.980), slight miscalibration resolved by recalibration, and preserved net benefit across most thresholds.

conclusionsA simple model based on early clinical and laboratory variables supports risk stratification for HLH in SFTS and may facilitate closer monitoring, repeated HLH assessment, and timely individualized management.