SFTS is a novel viral hemorrhagic fever with a wide distribution and a high mortality rate. Unfortunately, there is currently no effective treatment available for this condition. Consequently, it is of paramount importance to accurately identify patients who require intensive treatment upon admission.
Previous studies have explored predictive models for mortality in patients with severe fever and thrombocytopenia syndrome. One such study conducted by Li Wang [16] focused on a risk score model for predicting mortality in severe fever with thrombocytopenia. The model utilized the qSOFA and SIRS scoring systems, demonstrating remarkable sensitivity and specificity. However, implementing this model in primary hospitals with heavy workloads may pose challenges due to its complex nature. Another researcher, Shue Xiong [17], developed a mortality prediction model for patients with severe fever and thrombocytopenia syndrome. However, this model incorporated viral load as a laboratory indicator, which may not be readily accessible in most primary care hospitals. Given that the majority of SFTS patients initially seek treatment in primary care settings, it becomes crucial to identify predictive models that assess the risk of developing severe disease and can be widely applied in these primary care hospitals.
Our study revealed significant differences in age and the presence of neurological manifestations in relation to the severity of SFTS, which was consistent with previous findings [20–22]. These observations may be attributed to the increased vulnerability of elderly individuals to SFTSV infection and its severe complications due to their weakened immune responses. However, the underlying mechanism of nerve damage in this disease remains unclear.
Previous studies have reported associations between disease severity and various factors such as albumin, thrombocytopenia, creatine kinase, lactate dehydrogenase, activated partial thromboplastin time, blood urea nitrogen, and aspartate aminotransferase [23, 24], which aligns with our findings. Additionally, our study revealed that HCT-ALB is also associated with disease severity, providing novel insights specifically for patients with severe fever and thrombocytopenia syndrome. Through multivariate logistic regression analysis, we identified decreased platelet count (PLT) and prolonged activated partial thromboplastin time (APTT) as independent risk factors for disease severity in SFTS patients, consistent with the majority of previous studies [22, 23]. Furthermore, our results demonstrated that creatine kinase (CK), lactate dehydrogenase (LDH), and neutrophil percentage (NEUT%) are also independent risk factors for the severity of SFTS.
Nomograms, introduced to medicine by JL Henderson in 1928, are valuable tools for predicting the probability of outcomes in regression models [25]. As depicted in the nomogram plot (Fig. 2), higher NEUT%, CK, LDH, and APTT values in SFTS patients indicate an increased likelihood of developing severe illness during disease progression. Conversely, patients with higher PLT levels have a lower risk of severe disease. By summing the total score and locating it on the scale, the risk of developing severe disease in SFTS patients can be predicted. For instance, patients with a total score of 59 have a 50% risk of developing severe disease over the course of the illness. The discriminatory power and calibration of the prediction model were assessed using ROC curves and Hosmer-Lemeshow analysis. Based on this analysis, we emphasize the importance of evaluating patients' scores based on their laboratory results during disease management. This approach enables better differentiation between patients with severe and mild disease, facilitating timely and effective medical interventions for those with severe disease. Furthermore, this scoring system can be implemented to assess disease progression and eventual prognosis.
Moreover, APTT, PLT, NEUT%, CK, and LDH are standard laboratory indicators that are readily available, cost-effective, and reproducible. These indicators allow for early identification of high-risk patients, early intervention, dynamic assessment, and improvement of patient survival. These findings highlight the strength of our research.