PLoS neglected tropical diseases · 2026
A clinical scoring system for the early identification of superinfections in patients with severe fever with thrombocytopenia syndrome: An ambispective multicentre cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Li Yu-Yao, Zhang Jian-Kang, Zhang Chun, Pan Zhi-Ping, Liu Xiao, Liu Meng-Yu, Chen Qiang, Zhang Yuan-Yuan, Jiang Yuan, Yuan Xu-Ya, Liu Si-Yang, Li Xiang, Yu Fei-Dan, Gao Yu-Feng, Cheng Jun, Xie Qin-Xiu, Rao Jian-Guo, Zhu Li-Yu, Liu Zhen-Jun, Ye Ying, Li Jia-Bin, Hu Li-Fen
Аннотация
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.
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.