Study on the Nomogram Model of Syndrome Elements for Lobar Pneumonia Caused by Mycoplasma Infection in Children
Objective:To explore the characteristics of syndrome elements of mycoplasma pneumoniae pneumo-nia in children,construct a risk prediction model based on TCM syndrome elements and clinical data,and evaluate the prediction efficiency of the model.Methods:Medical records of 180 children with mycoplasma pneumoniae pneumonia who were hospitalized in the pediatrics department of the Affiliated Hospital of Shandong University of Chinese Medicine from September 2021 to August 2022 were retrospectively selected.The 180 children who met the inclusion criteria were selected as the study subjects,and were divided into the lobar pneumonia group(80 cas-es)and non-lobar pneumonia group(100 cases)according to whether they were diagnosed with lobar pneumonia.Single factor analysis and binary Logistic regression analysis were used to screen the predictors,and a preliminary diagnosis prediction model was established.The model was evaluated by Hosmer-Lemeshow fit test and receiver operating characteristic curve(ROC curve),and a nomogram was drawn.Results:Binary Logistic regression analy-sis showed that age,vomiting of phlegm-drool,LDH,TCM syndrome elements of dampness and heat were the risk factors for lobular pneumonia caused by mycoplasma pneumoniae infection in children(P＜0.05).Based on the above predictive factors,the Logistic regression model P=1/[1+exp(-1.794X1+0.014X2+1.668X3+0.503X4+0.293X5-6.23)]was constructed and presented in the form of a column graph.Hosmer-Lemeshow test showed that the model had a good fit(x2=7.775,P=0456),the area under ROC curve was 0.834,the diagnostic sensitivity was 92.5％,and the diagnostic specificity was 63％.Conclusion:The risk prediction model of mycoplasma pneu-moniae infection with lobar pneumonia in children was established,which included age,vomiting of phlegm-drool,lactate dehydrogenase,TCM syndrome elements of heat and dampness.The model has good fitting degree and accu-racy,and can be used as a reference for diagnosis and prediction in clinic.
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