Establishment and validation of a predictive model for moderate and severe respiratory syncytial virus infection in infants

Objective To explore the risk factors for moderate and severe respiratory syncytial virus （RSV） infection in infants， and to establish and validate the predictive model. Methods Clinical data of 399 children with RSV infection were retrospectively analyzed， including 299 cases in the model group and 100 cases in the validation group. Univariate and multivariate Logistic regression analyses were used to screen the risk factors of moderate and severe RSV infection， and a clinical scoring model was established. Results In the model group （n = 299）， 48 children were classified with moderate to severe RSV infection and 251 cases of mild RSV infection. According to univariate and multivariate Logistic regression analyses， body weight， feeding history， wheezing， erythrocyte distribution width and hematocrit were the risk factors （all P < 0.05）， which were used to fit the joint diagnosis and establish the clinical scoring model. The area under the ROC curve （AUC） of clinical scoring model was 0.777 （95%CI 0.703-0.853）， the diagnostic cutoff value was 1.365， the sensitivity was 0.829 and the specificity was 0.604， respectively. The internal validation results showed that the model had high consistency. Conclusion A clinical scoring model for predicting moderate and severe RSV infection is established， which has certain accuracy.