5岁以下儿童重症肺炎支原体肺炎预测模型的构建OA

Objective To establish a predictive model for severe Mycoplasma pneumoniae pneumonia(SMPP)in children younger than 5 years.Methods Clinical data of 504 children younger than 5 years with Mycoplasma pneumoniae pneumonia admitted to Xiaogan Hospital of Wuhan University of Science and Technology from January to December 2023 were retrospectively analyzed.Based on discharge diagnosis,patients were classified into a non-SMPP group(n=345)and an SMPP group(n=159).Univariate analysis and LASSO regression were used to screen predictors of SMPP.The selected variables were then entered into a multivariable logistic regression to construct the prediction model,and its performance was evaluated.Results Multivariable logistic regression identified lung imaging findings(proportion with consolidation),duration of fever,high-sensitivity C-reactive protein,lactate dehydrogenase,creatine kinase,and lymphocyte-to-neutrophil ratio as predictors of SMPP(P<0.05).The model based on these six indicators achieved an area under the receiver operating characteristic curve of 0.862(95%CI:0.824-0.900),with a sensitivity of 85.8%and a specificity of 77.4%.The calibration curve was close to the ideal curve,and Spiegelhalter's Z test indicated good calibration(P=0.313).Decision curve analysis showed a net benefit across a threshold probability range of 0.75%-100%,indicating high clinical applicability.Conclusions The predictive model based on lung imaging findings(proportion with consolidation),duration of fever,high-sensitivity C-reactive protein,lactate dehydrogenase,creatine kinase,and lymphocyte-to-neutrophil ratio shows good performance for predicting SMPP in children younger than 5 years.