Abstract
BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging zoonotic infectious disease caused by the SFTS virus and is characterized by a high mortality rate. The primary objective of this study was to investigate high-mortality risk factors in SFTS and to create a nomogram model for personalized prediction. METHODS: A total of 523 patients with SFTS who were admitted to the Second Hospital of Nanjing, Nanjing University of Chinese Medicine, between January 2020 and December 2023 were retrospectively analyzed: 75 cases were classified in the death group and 448 cases in the survival group. Development of a predictive nomogram model was based on the independent risk factors that were stepwise screened through univariate analysis, LASSO analysis (least absolute shrinkage and selection operator), and multivariate logistic regression analysis. RESULTS: Based on stepwise variable screening by univariate analysis, LASSO analysis, and multivariate logistic regression, the following were independent mortality risk factors in patients with SFTS: age (odds ratio [OR], 1.06; 95% CI, 1.03-1.10; P < .001), hemorrhagic symptoms (OR, 3.39; 95% CI, 1.31-8.78; P = .012), neurologic symptoms (OR, 4.89; 95% CI, 2.72-8.77; P < .001), platelet count (OR, 0.99; 95% CI, .98-.99; P = .045), prothrombin time (OR, 1.32; 95% CI, 1.11-1.56; P = .001), activated partial thromboplastin time (OR, 1.02; 95% CI, 1.01-1.03; P = .007), and viral load ≥10(7)copies/mL (OR, 2.66; 95% CI, 1.36-5.20; P = .004). The area under the curve (0.87; 95% CI, .832-.909) showed excellent predictive power. Calibration curves showed the accuracy of the assessed nomograms. Decision curve analysis results showed a greater net benefit when the threshold probability of patient death was between 0.02 and 0.75. CONCLUSIONS: A nomogram model consisting of 7 risk factors was successfully constructed, which can be used to predict SFTS mortality risk factors early.