A Model Based on Meta-analysis to Evaluate Poor Prognosis of Patients with Severe Fever with Thrombocytopenia Syndrome

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2024 Jan 23

PMID 38260897

Authors

Zishuai Liu

Zhouling Jiang

Ligang Zhang

Xiaoyu Xue

Chenxi Zhao

Yanli Xu

Wei Zhang

Ling Lin

Zhihai Chen

Affiliations

Soon will be listed here.

Abstract

Background: Early identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.

Method: Retrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.

Result: A total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002-1.063, = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669-0.889, <0.001). The validation cohort was divided into four risk groups with cut-off values. Compared with the low-medium risk group, the mortality rate of high-risk and very high-risk patients was more significant (RR =5.677, 95% CI 4.961-6.496, <0.001).

Conclusion: The prediction model for the fatal outcome of SFTS patients has shown positive outcomes.https://www.crd.york.ac.uk/prospero/ (CRD42023453157).

Citing Articles

Aspartate aminotransferase to platelet ratio correlates with poor prognosis and metabolic alterations in infection.

Guo C, Li R, Wang X, Peng X Front Immunol. 2025; 15:1471511.

PMID: 39896801 PMC: 11781988. DOI: 10.3389/fimmu.2024.1471511.

Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome.

He Q, You Z, Dong Q, Guo J, Zhang Z Front Microbiol. 2024; 15:1458670.

PMID: 39345257 PMC: 11428110. DOI: 10.3389/fmicb.2024.1458670.

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