Risk Factor Analysis and Prediction Model Construction of Severe Adenovirus Pneumonia in Children
Objective To explore the risk factors for severe adenovirus pneumonia in children,construct a risk prediction model,draw a clinical decision curve,and provide guidance for clinical diagnosis,treatment and prognosis.Methods A retrospective analysis was conducted for 65 children hospitalized with adenovirus pneumonia from January 2017 to December 2021,to explore the clinical fea-tures and risk factors of children with severe adenovirus pneumonia,construct a risk prediction model and draw a nomogram.Bootstrap re-sampling was used for model internal validation.The quantification of model performance used area under the curve(AUC)of receiver op-erating characteristic(ROC)curve and calibration curve for fit testing and calibration.Draw the clinical decision curve to analyze the clinical utility of the model.Results The results of univariate analysis showed that there were statistically significant differences in hospi-tal stays,cough days,wheezing,trident sign and lactate dehydrogenase(LDH,P<0.05).The results of multivariate Logistic regression analysis showed that the hospital days,wheezing and LDH were significantly associated with adenovirus pneumonia.The nomogram showed that the probability of severe adenovirus pneumonia was 80.3%.The results of the prediction model showed that the AUC affecting the in-fection of children with severe adenovirus pneumonia was 0.899,and its sensitivity was 80.0%,and its specificity was 80.0%,and the prediction effect was good.The calibration curve showed a good agreement between the actual probability and predicted probability.The clinical decision curve showed a clear net benefit of the model for almost all threshold probabilities.Conclusion Adenovirus pneumonia tends to occur in infants.Hospital stays,LDH level and wheezing can be used as indicators to predict the severity of adenovirus pneumoni-a in children,and provide a guiding basis for clinical diagnosis and treatment and prognosis.
NETL
NSTL
万方数据
