Estabilshment And Validation Of Clinical Model In Febrile Children With Convulsion

Objective(s)A retrospective study was conducted to collect the clinical laboratory and symptomatological indicators of the study subjects,and an attempt was made to construct a clinical diagnostic model that may be applied to assist in the differential diagnosis of febrile and convulsive diseases in clinical practice through multivariate logistic algorithms.MethodsIn this study,a retrospective study was conducted to collect 180 children hospitalized in our pediatric department from January 2017 to July 2022 who underwent lumbar puncture surgery and were diagnosed as febrile seizures,epilepsy with fever,central nervous system infection,and autoimmune encephalitis.Obtain clinical data of the study object through the hospital system.Through single factor analysis and multiple factor analysis,data with characteristics for disease diagnosis are selected,and R software is used to incorporate the most characteristic data.A clinical prediction model(clinical diagnostic model)for differential diagnosis is constructed using multiple logistic algorithms.Results1.1Model 一: A disease diagnostic model of febrile seizures versus epilepsy with fever,central nervous system infection and autoimmune encephalitis.1.2Univariate analysis of collected clinical indicators showed that peak body temperature,number of convulsions,whole blood red blood cells,hemoglobin,cerebrospinal fluid IgG,cerebrospinal fluid ADA,and cerebrospinal fluid IgG index were statistically significant in the identification of epilepsy with fever,central nervous system infections,and autoimmune encephalitis in febrile seizures(P<0.05).Further multivariate logistic regression analysis was performed on the data with significant differences in univariate analysis.The results showed that three charact-eristic data(convulsion frequency,whole blood red blood cells,and cerebro-spinal fluid IgG)with P<0.05 were included in the construction of the disease diagnosis model.In Model 1,the AUC of the training set and the verification set are 0.832 and 0.783,respectively;The basic trend of the calibration line and the standard for the training set and the verification set are basically consistent,with an average absolute error of0.016 and 0.044,respectively.The results show that the diagnostic model can correctly classify 75.2% of samples,with a sensitivity of 70.4% and a specificity of 77.9%.2.1 Model 二:A disease diagnostic model of central nervous system infection and autoimmune encephalitis versus febrile seizures and epilepsy with fever.2.2Univariate analysis of collected clinical indicators showed that Duration of fever,peak body temperature,percentage of neutrophils,percentage of eosinophils,percentage of basophils,absolute value of neutrophils,peripheral blood red blood cells,hemoglobin,blood ammonia,indirect bilirubin γ-glutamyltranspeptidase,blood potassium,blood sodium,blood chloride,blood calcium,blood sodium/blood potassium,lactate dehydrogenase,lactate dehydrogenase isoenzyme,blood complement C3,cerebrospinal fluid IgG,cerebrospinal fluid IgA,cerebrospinal fluid leukocytes,cerebrospinal fluid AST,cerebrospinal fluid blood glucose,cerebrospinal fluid chloride,and cerebrospinal fluid IgG index,showed statistical significance in distinguishing central nervous system infection and autoimmune encephalitis versus febrile seizures and epilepsy with fever(P<0.05).Further multivariate logistic regression analysis was conducted on data with significant differences in univariate analysis,and four characteristic variables(absolute value of neutrophils,blood complement C3,cerebrospinal fluid leukocytes,and cerebrospinal fluid IgG index)with P<0.05 were included in the construction of the disease diagnosis model.In Model 2,the AUC of the training set and validation set are 0.795 and 0.749,respectively;The trend of calibration curve and standard curve of training set and verification set is basically the same,and the average absolute error is 0.034 and 0.039 respectively.The results indicate that the diagnostic model can correctly classify 76.0% of samples,with a sensitivity of 58.0% and a specificity of 88.7%.3.1Model 三:A disease diagnostic model of epilepsy with fever versus febrile seizures,central nervous system infection and autoimmune encephalitis.3.2 Single factor analysis of collected clinical indicators showed that fever duration,peak body temperature,number of convulsions,percentage of neutrophils,percentage of lymphocytes,percentage of basophils,absolute value of neutrophils,NLR,alkaline phosphatase,blood sodium,blood chlorine,blood phosphorus,sodium/potassium,lactate dehydrogenase,cerebrospinal fluid leukocytes,cerebrospinal fluid AST were statistically significant in epilepsy with fever versus febrile seizures,central nervous system infection and autoimmune encephalitis(P<0.05).Further multivariate logistic regression analysis was performed on data with significant differences in univariate analysis.The results showed that P<0.05,four characteristic variables(peak body temperature,number of convulsions,lactate dehydrogenase in blood,and cerebrospinal fluid AST)were included in the construction of the disease diagnosis model.In model 3,the AUC of the training set and the verification set are 0.918 and0.853,respectively;The basic trend of the calibration line and the standard for the training set and the verification set are basically consistent,with an average absolute error of 0.02 and 0.048,respectively.The results show that the diagnostic model can correctly classify 76.8% of samples,with a sensitivity of 63.6% and a specificity of81.8%.Conclusion(s)1.The number of convulsions,whole blood red blood cells,and cerebrospinal fluid IgG can be used as clinical indicators to distinguish febrile seizures versus epilepsy with fever,central nervous system infection and autoimmune encephalitis.The higher the whole blood red blood cell value,the lower the number of convulsions and the lower the cerebrospinal fluid IgG,the more likely the diagnosis to be febrile convulsions.2.The absolute value of neutrophils,blood complement C3,cerebrospinal fluid leukocytes,and cerebrospinal fluid IgG index can be used as clinical indicators for differentiating central nervous system infection and autoimmune encephalitis versus febrile seizures and epilepsy with fever.The higher the absolute value of neutrophils,cerebrospinal fluid leukocytes,and cerebrospinal fluid IgG index,the lower the blood complement C3 value,the more likely it is to be diagnosed as central nervous system infection and autoimmune encephalitis.3.Peak body temperature,frequency of convulsions,lactate dehydrogenase in blood,and cerebrospinal fluid AST can be used as clinical indicators for the diagnosis of epilepsy with fever versus febrile seizures,central nervous system infection and autoimmune encephalitis.The higher the number of convulsions,the lowerr the body temperature peak,blood lactate dehydrogenase,and cerebrospinal fluid AST values,the more likely the diagnosis to be epilepsy.