A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study

Yu-Yao Li, Jian-Kang Zhang, Chun Zhang, Zhi-Ping Pan, Xiao Liu, Meng-Yu Liu, Qiang Chen, Yuan-Yuan Zhang, Yuan Jiang, Xu-Ya Yuan, Si-Yang Liu, Xiang Li, Fei-Dan Yu, Yu-Feng Gao, Jun Cheng, Qin-Xiu Xie, Jian-Guo Rao, Li-Yu Zhu, Zhen-Jun Liu, Ying Ye, Jia-Bin Li and Li-Fen Hu

PLOS Neglected Tropical Diseases, 2026, vol. 20, issue 9, 1-16

Abstract: Background: Superinfection is a major contributor to mortality in patients with severe fever with thrombocytopenia syndrome (SFTS). However, early identification remains challenging because detection of the pathogen causing the superinfection is often delayed. Objective: This study aims to develop a clinical scoring system for the early identification of superinfection in SFTS patients. Methods: Among 1942 SFTS patients from 4 hospitals in China, a retrospective cohort (2014–2024) was used for model development, with 1182 patients from 2 hospitals split into training (n = 823) and internal validation (n = 359) sets, and 475 patients from 2 additional hospitals for external validation sets. Four machine learning algorithms were evaluated, with the optimal model converted into a simplified scoring scale. Finally, a prospective cohort (n = 285, 2025) evaluated the real-world performance. Model efficacy was comprehensively assessed using the area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), sensitivity, and specificity. Results: Among the 36 variables, 8 predictors (age, ALB, AST, BUN, GLU, CRP, HGB, and expectoration) for superinfection were identified by ensembling five machine learning algorithms. The RF, XGBoost, LightGBM, and LR prediction models were constructed using the 8 predictors. LR demonstrated optimal performance, with an AUROC of 0.839 (95% CI: 0.797–0.880) in the internal validation set and 0.805 (95% CI: 0.764–0.847) in the external validation set. In the real-world prospective study, the model maintained high predictive efficacy (AUROC: 0.854). Finally, the model’s nomogram was simplified into a novel 3-tiered risk scoring scale to enhance clinical applicability. Conclusions: Our research developed a dynamic, early diagnostic tool that could improve real-time prediction of superinfection risk at the bedside and enhance antimicrobial stewardship. Author summary: Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening tick-borne disease. It is characterized by a high incidence of superinfections, which substantially increase the risk of mortality. Unfortunately, delayed identification of superinfections often leads to worse outcomes or inappropriate antibiotic use in SFTS patients. In this multicenter study spanning 12 years, we developed a data-driven clinical prediction model designed to stratify the risk of superinfection in SFTS patients using four machine learning algorithms. The model’s performance and clinical utility were demonstrated through receiver operating characteristic curve analysis, calibration assessment, decision curve analysis, sensitivity, and specificity. Logistic regression was identified as the optimal algorithm, with strong predictive accuracy across internal and external validation. Its efficacy was further confirmed in a prospective real-world cohort, which achieved an excellent discriminatory power. Ultimately, the model was simplified into a three-tier risk scoring system, enabling rapid bedside assessment of superinfection risks without the need for complex computational tools. Overall, by identifying key clinical indicators, our model provides clinicians with an effective tool for the rapid identification of superinfection risk in SFTS patients using easily accessible predictors.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pntd00:0014696

DOI: 10.1371/journal.pntd.0014696

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