A Scoring Model for Predicting the Occurrence of Severe Illness in Hospitalized Patients with SFTS

抄録

Severe fever with thrombocytopenia syndrome is an emerging hemorrhagic fever disease with high mortality. Severe cases progressed rapidly, with deaths occurring within two weeks. Therefore, the construction of a model to predict the disease progression among hospitalized SFTS infected patients plays an important role in clinical practice. The development cohort including 121 SFTS patients, 25 were severe and 96 were mild. 2 of the 64 variables were independent risk factors, including neurological symptoms (OR, 12.915; 95% CI, 3.342-49.916; P<0.001) and AST/ALT levels (OR, 1.891; 95% CI, 1.272-2.813; P=0.002). The AUC of the model was 0.882 (95%CI: 0.808-0.956). The mean AUC value obtained from 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 for helping identifying severe patients as early as possible with high predictive value, stability, and repeatability. This model can help clinicians in their treatment plan making.