Scoring Model for Predicting the Occurrence of Severe Illness in Hospitalized Patients with Severe Fever with Thrombocytopenia Syndrome

Japanese journal of infectious diseases

article

Xuemin Wei¹  Lirui Tu²  Ling Qiu²  Mengting Chen¹  Yao Wang¹  Mengyu Du¹  Haopeng Kan¹  Qing Dong²  Xiaoying Xu¹  Haowen Yuan¹  Li Zhao¹  Hongling Wen¹ 

[1] Department of Microbiological Laboratory Technology, School of Public Health, Cheeloo College of Medicine, Shandong University;Department of Infection, Shandong Provincial Public Health Clinical Center

关键词: Severe fever with thrombocytopenia syndrome;    risk model;    prediction;    severe illness;   

DOI  :  10.7883/yoken.JJID.2021.716

学科分类：传染病学

来源: National Institute of Infectious Diseases

【 摘 要 】

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging hemorrhagic fever with high mortality. Severe cases progressed rapidly, with deaths occurring within 2 weeks. Therefore, constructing a model to predict disease progression among hospitalized patients plays an important role in clinical practice. The development cohort included 121 patients with SFTS, 25 with severe SFTS, and 96 with mild SFTS. Two of the 64 variables were independent risk factors, including neurological symptoms (odds ratio [OR], 12.915; 95% confidence interval [CI], 3.342–49.916; P < 0.001) and aspartate aminotransferase/alanine aminotransferase levels (OR, 1.891; 95% CI, 1.272–2.813; P = 0.002). The model’s area under the curve (AUC) was 0.882 (95% CI: 0.808–0.956). The mean AUC value obtained from the internal validation was 0.883 (95% CI: 0.809–0.957). The AUC in the external validation cohort was 0.873 (95% CI: 0.775–0.972). This model can be used to identify severely ill patients as early as possible with high predictive value, stability, and repeatability. This model can help clinicians with their treatment plans.

【 授权许可】

Unknown