Treating severe hemorrhagic fever with renal syndrome (HFRS) cases is difficult. There is currently no early warning model for patients with severe HFRS. Data from 235 patients with HFRS between January 2013 and December 2019, as well as 394 laboratory indicators, were retrospectively collected. A multivariate logistic regression model was used to construct an early warning model for severe diseases. The model's accuracy was evaluated based on the area under the receiver operating characteristic curve. The area under the curve of the early warning models for both exceeded 0.9 for the two stages. In the febrile stage, there were significant differences between the severe and mild groups (P < 0.05) in renal estimated glomerular filtration rate (eGFR), urinary leukocytes, electrolytes, urine conductivity, and urinary epithelial cell count. In the nonfebrile stage, there were significant differences between the severe and mild groups (P < 0.05) in renal eGFR, electrolytes, urine conductivity, and renal cystatin C levels. The two early warning models were well-fitted and exhibited excellent predictive performance. This can help clinicians gain time to provide appropriate preemptive treatment to avoid the further development of severe disease and reduce the mortality rate.
Keywords: clinical study; early warning models; hemorrhagic fever with renal syndrome.