Establishment of a diagnosis model for pediatric mycoplasma pneumonia and lobar pneumonia
Objective To establish a diagnostic model for children with mycoplasma pneumoniae(MP)complicated with lobar pneumonia by Logistics regression.Methods The clinical data of 239 children hospitalized with Mycoplasma pneumoniae between June 2018 and June 2023 were retrospectively analyzed and divided into the conventional pneumonia group(78 cases)and the lobar pneumonia group(161 cases).Lasso regression model was utilized to screen the relevant features of lobar pneumonia,and Logistics regression was applied to construct a diagnostic prediction model.Results Univariate analysis revealed that age,cough history,white blood cells count(WBC),neutrophil count(NEU),platelets(PLT),lactate dehydrogenase(LDH)were associated with lobar pneumonia.The age,cough history,WBC,NEU,CRP,LDH,and bolus screened by lasso regression were strongly associated with lobar pneumonia.Logistics regression analysis showed that age,cough history,WBC,LDH,and bolus were independent diagnostic factors in patients with lobar pneumonia.The Nomogram model showed good discrimination and clinical utility in distinguishing pediatric MP from lobar pneumonia.differentiation and clinical utility.Conclusion Lasso and Logistic regression are studied to construct a column-line graph model,and age,cough history,WBC,LDH,and talk embolism are found to be independent diagnostic factors for MP complicated with lobar pneumonia.And the model has a C-index of 0.803,which is highly accurate and helps clinical decision-making.
NETL
NSTL
万方数据
