Dataset Information

A scoring model for predicting prognosis of patients with severe fever with thrombocytopenia syndrome.

ABSTRACT: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging epidemic infectious disease caused by the SFTS bunyavirus (SFTSV) with an estimated high case-fatality rate of 12.7% to 32.6%. Currently, the disease has been reported in mainland China, Japan, Korea, and the United States. At present, there is no specific antiviral therapy for SFTSV infection. Considering the higher mortality rate and rapid clinical progress of SFTS, supporting the appropriate treatment in time to SFTS patients is critical. Therefore, it is very important for clinicians to predict these SFTS cases who are more likely to have a poor prognosis or even more likely to decease. In the present study, we established a simple and feasible model for assessing the severity and predicting the prognosis of SFTS patients with high sensitivity and specificity. This model may aid the physicians to immediately initiate prompt treatment to block the rapid development of the illness and reduce the fatality of SFTS patients.

SUBMITTER: Jia B

PROVIDER: S-EPMC5626493 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A scoring model for predicting prognosis of patients with severe fever with thrombocytopenia syndrome.

Jia Bei B Yan Xiaomin X Chen Yuxin Y Wang Guiyang G Liu Yong Y Xu Biyun B Song Peixin P Li Yang Y Xiong Yali Y Wu Weihua W Hao Yingying Y Xia Juan J Zhang Zhaoping Z Huang Rui R Wu Chao C

PLoS neglected tropical diseases 20170921 9

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging epidemic infectious disease caused by the SFTS bunyavirus (SFTSV) with an estimated high case-fatality rate of 12.7% to 32.6%. Currently, the disease has been reported in mainland China, Japan, Korea, and the United States. At present, there is no specific antiviral therapy for SFTSV infection. Considering the higher mortality rate and rapid clinical progress of SFTS, supporting the appropriate treatment in time to SFTS patients i ...[more]

PMID: 28934195

Similar Datasets

Project description:Severe fever with thrombocytopenia syndrome (SFTS) virus (SFTSV) is an emerging tick-borne phlebovirus with a high fatality rate. Previous studies have demonstrated the poor prognostic role of eosinophils (EOS) and basophils (BAS) in predicting multiple viral infections. This study aimed to explore the role of EOS and BAS in predicting prognosis of patients with SFTS. A total of 194 patients with SFTS who were admitted to Yantai City Hospital from November 2019 to November 2021 were included. Patients' demographic and clinical data were collected. According to the clinical prognosis, they were divided into survival and non-survival groups. Independent risk factors were determined by univariate and multivariate logistic regression analyses. There were 171 (88.14%) patients in the survived group and 23 (11.86%) patients in the non-survived group. Patients' mean age was 62.39 ± 11.85 years old, and the proportion of males was 52.1%. Older age, neurological manifestations, hemorrhage, chemosis, and increased levels of laboratory variables, such as EOS% and BAS% on admission, were found in the non-survival group compared with the survival group. EOS%, BAS%, aspartate aminotransferase (AST), direct bilirubin (DBIL), and older age on admission were noted as independent risk factors for poor prognosis of SFTS patients. The combination of the EOS% and BAS% had an area under the curve (AUC) of (0.82; 95% CI: 0.725, 0.932, P = 0.000), which showed an excellent performance in predicting prognosis of patients with SFTS compared with neutrophil-to-lymphocyte ratio (NLR), and both exhibited a satisfactory performance in predicting poor prognosis compared with De-Ritis ratio (AST/alanine aminotransferase (ALT) ratio). EOS% and BAS% were positively correlated with various biomarkers of tissue damage and the incidence of neurological complications in SFTS patients. EOS% and BAS% are effective predictors of poor prognosis of patients with early-stage SFTS. The combination of EOS% and BAS% was found as the most effective approach.

Dataset Information

A scoring model for predicting prognosis of patients with severe fever with thrombocytopenia syndrome.

Publications

A scoring model for predicting prognosis of patients with severe fever with thrombocytopenia syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets