RESUMEN
Objective:To construct a clinical predictive model of severe adenovirus pneumonia(SAP)in children using random forest and verify it.Methods:The clinical, laboratory and imaging data of 542 children with adenovirus pneumonia treated in Tianjin Children′s Hospital from January 2019 to January 2021 were analyzed retrospectively.The research object was randomly divided into training dataset and validation dataset(8∶2).The training dataset screened the predictors of SAP of pneumonia through random forest and established a prediction model, and the prediction model was expressed visually by the nomogram.In the validation dataset, the receiver operating characteristic curve(ROC)and sensitivity, specificity, error rate and confusion matrix were used to validate it.Results:A total of 439 children were in the training dataset, and 187 cases(42.60%)of the training data was divided as severe type.A total of 103 children were in validation dataset, and 44 cases(42.71%)of the validation dataset was divided as severe type.The percentage of monocytes(M%), PLT, AST, IL-6, the peak of body temperature, pulmonary inflammation of the consolidation and patchy shadowing were independent predictors of SAP in children.The area under the ROC curve of the training dataset and the validation dataset was 0.95(95% CI: 0.92~0.98)and 0.92(95% CI: 0.82~0.99), respectively.The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the training dataset were 0.994, 1.000, 0.987, 0.998, 1.000 and in validation dataset were 0.752, 0.990, 0.514, 0.945 and 0.857, respectively. Conclusion:The predictive model has good discriminant ability, and the early clinical and hematological indexes are helpful to improve the identification and screening of SAP in children.