HFRS is an infectious disease of global concern caused by hantavirus infection, which is characterized by increased vascular permeability, acute thrombocytopenia and renal damage. China is one of the most popular countries in the world . HFRS patients can be clinically manifested as mild, moderate, severe, and critical. Generally, HFRS caused by HTNV and SEOV infection is more serious, with a mortality rate of 5–15% . The purpose of this study is to analyze the clinical characteristics and laboratory examination of patients with HFRS, so as to establish a nomogram to predict the severity of the disease. Through this simple and feasible prediction model, we can identify the patient's condition early and provide patients with better medical measures in a timely manner to reduce patient mortality.
The typical course of HFRS can be divided into five different stages: fever, hypotension, oliguria, polyuria and recovery. In the hypotension stage, 1/3 of the deaths of HFRS patients are related to irreversible shock, while thrombocytopenia and leukocytosis are the characteristics of this stage. Thrombocytopenia can cause petechiae of the skin or mucous membranes, conjunctival congestion, hematemesis, hemoptysis, hematuria, and fatal intracranial hemorrhage . In addition, platelet dysfunction may also lead to abnormal blood coagulation . In the training cohort of Table 2, there were 63 seriously ill patients, including 2 patients with pulmonary hemorrhage, 5 patients with gastrointestinal hemorrhage and 2 patients with intracranial hemorrhage. However, there is no statistical difference between severe and mild patients due to the small sample size observed.
In this study, the platelets count decreased more significantly in the severe group. At the same time, after the parameter λ was selected by the 10-fold cross-validation based on the minimum standard in the LASSO model, the platelets count was also included in the regression model, indicating that platelets count can be used as a predictor of the severity of HFRS patients.
In patients with viral hemorrhagic fever, platelets can cause abnormal homeostasis and inflammatory activation, thereby inhibiting the body's antiviral immune response, and making patients show a high level of viremia. This mechanism leads to the aggravation of the patient's condition . Other studies have shown that WBC, PLT, platelet distribution width (PDW) and PCT can be used as valuable parameters for the severity of HFRS patients, especially the change of PDW on the first day of hospitalization is related to the survival rate of severe HFRS patients and can be used as potential predictors . In this study, the increase of WBC in patients with severe HFRS was significantly higher than that in mild patients, whereas a study showed that compared with leukocytosis, thrombocytopenia may better predict the prognosis of severe acute kidney injury (AKI) in patients with acute HTNV infection . Neutrophil activation is usually common in bacterial infections. It is interesting to note that markers of neutrophil activation, such as myeloperoxidase (MPO), human neutrophil elastase (HNE), histone and interleukin-8 (IL-8), are significantly increased in the blood and tissue of patients with severe HFRS. These results suggest that neutrophils can be activated by endothelial cells infected by hantavirus and may help to determine the degree of renal pathological damage in patients with severe HFRS . In our study, neutrophil in patients with severe HFRS was also higher than that in mild patients, which may further support this view from a clinical perspective.
Acute renal failure can occur in patients with severe HFRS, usually caused by tubulointerstitial and glomerular damage . In addition, the increase of platelet production and platelet activation may cause intravascular coagulation, the accumulation of inflammatory cells, and the release of pro-inflammatory cytokines in the kidney tissue, which can also lead to kidney damage [23, 24]. In this study, renal function impairment indicators such as urine protein, urea nitrogen, creatinine, and cystatin C were significantly increased in severe HFRS patients. Previous studies have also confirmed that plasma cystatin C and alpha-1-microglobulin (A1M) can be used as early and sensitive markers of renal injury in patients with HFRS, and can predict AKI [25, 26]. The complexity adjustment of LASSO regression model is controlled by the parameter λ, so as to avoid over-fitting. The larger the λ, the greater the penalty for a linear model with more variables, and a model with fewer variables is finally obtained , so in the end only creatinine is included in the prediction model. Patients with acute renal failure are often accompanied by hypocalcemia. Wang B  studied the prognostic ability of serum calcium in patients with severe AKI, and the results showed that low Ca concentration was an independent predictor of all-cause mortality in patients with severe AKI. Similarly, in our study, the average serum calcium concentration in HFRS patients was lower than normal level, especially in severely ill patients.
In addition, patients with HFRS can also experience acute cardiovascular events such as acute myocardial infarction and stroke, indicating that the increased levels of myocardial injury indicators such as creatine kinase, CK-MB and myoglobin can predict the risk of disease progression in patients . Another study showed that hypoproteinemia in patients with acute HFRS is associated with the severity of the patient's disease, which is consistent with our findings . The clinical manifestations of HFRS patients are diverse, including fever, headache, fatigue, myalgia, back pain and so on . In addition to the above symptoms in this study, gastrointestinal symptoms such as nausea, vomiting, diarrhea, abdominal distension and respiratory symptoms for example cough and dyspnea were also manifested. Severe HFRS patients may initially present as dry cough, followed by tachycardia, dyspnea, and then may rapidly progress to non-cardiogenic pulmonary edema, hypotension and circulatory failure, with a case-fatality rate of about 45% .
Based on LOSSA regression, we finally included six predictive indicators: "Neutrophils", "Hb", "Platelets", "Creatinine", "Ca" and "Dyspnea" to establish a nomogram. The AUC value of the nomogram is greater than 0.9 in both the training cohort and the verification cohort, indicating that the predictive model has a high value. Both the calibration plot and Hosmer-Lemeshow goodness-of-fit test show that the prediction probability of nomogram is in good agreement with the real results. In addition, in order to evaluate the clinical effectiveness of nomogram, we apply DCA to provide observations of clinical results based on threshold probability, from which net benefits can be derived (Net benefit is defined as the proportion of true positives minus the proportion of false positives, weighted by the relative harm of false positive and false negative results) [15, 32]. In this study, if the threshold probability of the patient or doctor is between 0 and 1, the use of nomogram to assess the risk of severe illness in HFRS patients can benefit patients. Clinical impact curve also intuitively shows that the nomogram has a better overall net benefit within a wide range of threshold probability and affecting the prognosis of patients.
However, our research also has some limitations. First, it is designed to be retrospective, and the inherent limitations of this type of research inevitably affect the choice of patients. Second, although we collected patient data from different periods of time to validate the model, it came from a single center. If possible, we still need cohorts from other research centers to validate the model. Finally, the number of cases in our study is relatively small, which may weaken the predictive ability of the current model.