Early Differential Diagnosis Model Of Severe Fever With Thrombocytopenia Syndrome And Hemorrhagic Fever With Renal Syndrome

Objective:In this study,we analyzed the basic information,early clinical symptoms and laboratory data of patients with fever with thrombocytopenia syndrome（SFTS）and hemorrhagic fever with renal syndrome（HFRS）.We used various statistical methods to establish a differential diagnosis model for fever with thrombocytopenia syndrome and hemorrhagic fever with renal syndrome（HFRS）and evaluated the application value of the model.METHODS:Baseline data,early clinical symptoms and laboratory findings of 113patients with fever with thrombocytopenia syndrome and 76 patients with hemorrhagic fever with renal syndrome diagnosed in our hospital from January 2015to June 2022 were retrospectively collected through our hospital electronic case system.In the model building group,the differences in early clinical characteristics and laboratory indicators between the two groups were analyzed by chi-square test,t-test and rank sum test,and the statistically insignificant indicators were excluded by single-factor regression,and the statistically significant indicators were substituted into the multi-factor stepwise logistic regression analysis to initially establish the differential diagnosis model of fever with thrombocytopenia syndrome and hemorrhagic fever in renal syndrome and draw ROC curves,sensitivity and specificity.The data of the validation group were substituted into the model and the ROC curve,sensitivity and specificity were plotted to evaluate the value of the model.Results:1.Age（T=--3.650,P<0.001）,gender（x2=11.209,P=0.001）,exposure history（x2=10.228,P=0.001）,season of onset（x2=9.90,P=0.019）,time from onset to consultation（x2=-3.653,P<0.001）combined with diabetes mellitus in both disease groups（x2=5.360,p=0.021）were statistically significant between the groups（p<0.05）.2.Comparing the clinical characteristics of the disease between the two groups,there were differences in muscle pain（x2=4.306,p=0.038）,altered mental status（x2=26.617,p<0.001）,and diarrhea（x2=5.975,p=0.015）between the groups,which were statistically significant（p<0.05）.3.There were differences between the two groups in white blood cell count,leukocytosis,leukopenia,red blood cell count,hemoglobin,anemia,albumin,creatine kinase,creatine kinase isoenzyme,lactate dehydrogenase,blood urea nitrogen,blood creatinine,C-reactive protein,normal C-reactive protein,calcitoninogen,and prothrombin time groups,which were statistically significant（p<0.05）.4.Univariate and multifactorial stepwise regression analysis sieve of the model building group initially identified altered mental status（OR=11.830,95%CI:3.293-42.503）,diarrhea（OR=11.830,95%CI:1.201-6.151）,leukopenia（OR=11.830,95%CI:2.059-56.875）,albumin（OR=11.830,95%CI:1.073-1.419）,nor mal C-reactive protein（OR=11.830,95%CI:1.624-18.370）predictors,and mod eled as follows:Logit（P）=-8.107+2.471*X₁+0.409*X₂+2.382*X₃+1.651*X₄+0.210*X₅+2.196*X₆,and the chi-square value of the Hosmer-Lemeshow test for the d iagnostic model prediction model was 5.382,with a P value of 0.716（>0.05）.5.Evaluation of the diagnostic model:The area under the ROC curve plotted for the model establishment group was 0.910,and the cut-off value at the maximum Jorden index（0.673）was the optimal cut-off value,which corresponded to a sensitivity of75.0%,specificity of 92.3%,positive predictive value of 86.9%,and negative predictive value of 78.4%;the ROC curve plotted for the model validation group data,and the area under the ROC curve was 0.846,and the cut-off value at the maximum Jorden index（0.592）was the optimal value,which corresponded to a sensitivity of75.9%,specificity of 83.3%,positive predictive value of 96.6%,and negative predictive value of 58.3%.Conclusion:In this study,we combined the clinical features and laboratory indices of fever with thrombocytopenia syndrome and hemorrhagic fever in renal syndrome,and initially identified a model with differential diagnostic value.The model had a predictive probability of 0.910（95%CI:0.862-0.958）,a sensitivity of 75.0%,a specificity of 92.3%,a positive predictive value of 86.9%and a negative predictive value of 78.4%,and a good clinical predictive value.