Construction of a new predictive score for severe fever with thrombocytopenia syndrome combined with bacterial/fungal infections based on clinical data
10.3760/cma.j.cn311365-20250303-00066
- VernacularTitle:基于临床数据构建SFTS合并细菌/真菌感染的新型预测评分
- Author:
Ran WANG
1
;
Yan DAI
1
;
Qinqin PU
1
;
Nannan HU
1
;
Ke JIN
1
;
Jun LI
1
Author Information
1. 南京医科大学第一附属医院感染病科，南京 210029
- Publication Type:Journal Article
- Keywords:
Infections;
Severe fever with thrombocytopenia syndrome;
Prediction model
- From:
Chinese Journal of Infectious Diseases
2025;43(4):202-209
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study the risk factors for combined bacterial/fungal infections in patients with severe fever with thrombocytopenia syndrome (SFTS) and to develop a novel and validated prediction model.Methods:The basic data and the results of the first laboratory examination after admission were retrospectively collected from patients diagnosed with SFTS who were hospitalized in the First Affiliated Hospital, Nanjing Medical University from January 2018 to December 2022. The patients were categorized into co-infected and non-co-infected groups according to whether they had co-infections with bacterial/fungal infections or not.Independent risk factors were screened by multivariate logistic regression analyses. A novel prediction model was constructed, and the predictive value of the model was assessed using receiver operating characteristic curve. Non-parametric tests and chi-square test were used for statistical analysis.Results:A total of 294 patients were included, and 62 cases were in the combined infection group including 39 cases of simple respiratory tract infections, 11 cases of simple bloodstream infections, four cases of simple urinary tract infections, four cases of respiratory tract combined with bloodstream infection, and four cases of respiratory tract combined with urinary tract infection. Acinetobacter baumannii was mostly found in bacterial infections, with a total of 19 strains, followed by Escherichia coli and Pseudomonas aeruginosa, both with seven strains. Aspergillus were mostly common in fungi, with a total of 16 strains which were all collected from patients with pulmonary infections. Compared with the non-co-infected group, patients in the co-infected group had longer hospital stays, with statistically significant differences ( Z=-6.18, P<0.001). The patients also had higher frequencies of bleeding symptoms, neurological symptoms, severe illness, and death, with statistically significant differences ( χ2=23.91, 16.37, 15.51 and 15.58, respectively, all P<0.001). The aspartate transaminase-to-platelet ratio index (APRI) was also higher in patients with coinfection, with a statistically significant difference ( Z=-4.64, P<0.001). Multivariate binary logistic regression showed that severe illness (odds ratio ( OR)=2.567, 95% confidence interval ( CI) 1.344 to 4.904, P=0.004), blood glucose level higher than 7.782 mmol/L ( OR=4.766, 95% CI 2.493 to 9.109, P<0.001), procalcitonin level higher than 0.228 μg/L ( OR=2.487, 95% CI 1.289 to 4.799, P=0.007), and APRI value higher than 6.268 ( OR=3.032, 95% CI 1.404 to 6.548, P=0.005) were the independent risk factors for co-infections in SFTS patients. Disease severity, blood glucose, procalcitonin, and APRI were combined to construct a novel predictive model: Infect-risk score=-3.331+ 0.654×severity (severe=1, non-severe=0)+ 0.160×blood glucose+ 0.066×procalcitonin+ 0.013×APRI. The AUC for this score was 0.764 (95% CI 0.698 to 0.830, P<0.001), with Youden index of 0.416, sensitivity of 0.839, and specificity of 0.578. Conclusions:Severe illness, blood glucose levels higher than 7.782 mmol/L, procalcitonin levels above 0.228 μg/L, and APRI values above 6.268 are independent risk factors for bacterial/fungal coinfection in SFTS patients. The constructed Infect-risk score model has good predictive value for bacterial/fungal coinfection in SFTS patients.
