A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study
8 affiliations
- 1Department of Infectious Diseases, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- 2Department of Infectious Diseases, Lu’an People’s Hospital, Lu’an, Anhui, China
- 3Department of Infectious Diseases, Chaohu Hospital of Anhui Medical University, Chaohu, Anhui, China
- 4Department of Infectious Diseases, Anqing Municipal Hospital, Anqing, Anhui, China
- 5Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- 6Anhui Province Key Laboratory of Infectious Diseases, Anhui Medical University, Hefei, China
- 7Department of Infectious Diseases, Public Health Clinical Center of Anhui Province, Hefei, China
- 8Programme in Emerging Infectious Diseases, Duke-National University of Singapore Medical School, Singapore, Singapore
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.
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BibTeX
@article{li2026clinical,
title = {{A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study}},
author = {Yu-Yao Li and Jian-Kang Zhang and Chun Zhang and Zhi-Ping Pan and Xiao Liu and Meng-Yu Liu and Qiang Chen and Yuan-Yuan Zhang and Yuan Jiang and Xu-Ya Yuan and Si-Yang Liu and Xiang Li and Fei-Dan Yu and Yu-Feng Gao and Jun Cheng and Qin-Xiu Xie and Jian-Guo Rao and Li-Yu Zhu and Zhen-Jun Liu and Ying Ye and Jia-Bin Li and Li-Fen Hu},
journal = {PLOS Neglected Tropical Diseases},
year = {2026},
month = sep,
doi = {10.1371/journal.pntd.0014696},
url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0014696},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study
AU - Yu-Yao Li
AU - Jian-Kang Zhang
AU - Chun Zhang
AU - Zhi-Ping Pan
AU - Xiao Liu
AU - Meng-Yu Liu
AU - Qiang Chen
AU - Yuan-Yuan Zhang
AU - Yuan Jiang
AU - Xu-Ya Yuan
AU - Si-Yang Liu
AU - Xiang Li
AU - Fei-Dan Yu
AU - Yu-Feng Gao
AU - Jun Cheng
AU - Qin-Xiu Xie
AU - Jian-Guo Rao
AU - Li-Yu Zhu
AU - Zhen-Jun Liu
AU - Ying Ye
AU - Jia-Bin Li
AU - Li-Fen Hu
PY - 2026
DA - 2026/09/10
JO - PLOS Neglected Tropical Diseases
DO - 10.1371/journal.pntd.0014696
UR - https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0014696
AB - 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.
SN - 1935-2735
ER -