Development and validation of a nomogram for predicting severe respiratory syncytial virus-associated bronchiolitis

Abstract

Background Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and is related to the severity of the disease. This study aimed to develop and validate a nomogram for predicting severe bronchiolitis in infants and young children with RSV infection. Methods A total of 325 children with RSV-associated bronchiolitis were enrolled, including 125 severe cases and 200 mild cases. A prediction model was built on 227 cases and validated on 98 cases, which were divided by random sampling in R software. Relevant clinical, laboratory and imaging data were collected. Multivariate logistic regression models were used to determine optimal predictors and to construct nomograms. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability and decision curve analysis (DCA). Results There were 137 (60.4%) mild and 90 (39.6%) severe RSV-associated bronchiolitis cases in the training group (n = 227) and 63 (64.3%) mild and 35 (35.7%) severe cases in the validation group (n = 98). Multivariate logistic regression analysis identified 5 variables as significant predictive factors to construct the nomogram for predicting severe RSV-associated bronchiolitis, including preterm birth (OR = 3.80; 95% CI, 1.39–10.39; P = 0.009), weight at admission (OR = 0.76; 95% CI, 0.63–0.91; P = 0.003), breathing rate (OR = 1.11; 95% CI, 1.05–1.18; P = 0.001), lymphocyte percentage (OR = 0.97; 95% CI, 0.95–0.99; P = 0.001) and outpatient use of glucocorticoids (OR = 2.27; 95% CI, 1.05–4.9; P = 0.038). The AUC value of the nomogram was 0.784 (95% CI, 0.722–0.846) in the training set and 0.832 (95% CI, 0.741–0.923) in the validation set, which showed a good fit. The calibration plot and Hosmer‒Lemeshow test indicated that the predicted probability had good consistency with the actual probability both in the training group (P = 0.817) and validation group (P = 0.290). The DCA curve shows that the nomogram has good clinical value. Conclusion A nomogram for predicting severe RSV-associated bronchiolitis in the early clinical stage was established and validated, which can help physicians identify severe RSV-associated bronchiolitis and then choose reasonable treatment.

Personalized Infant Risk Prediction for Severe Respiratory Syncytial Virus Lower Respiratory Tract Infection Requiring Intensive Care Unit Admission

“…Other tools that include predictors that are not routinely captured in clinical care or not available near birth are not practical in making clinical decisions [ 20 ], whereas our tool uses information routinely captured and electronically available at or shortly after birth. Tools have also been developed to predict escalation of care at the time of LRTI presentation, usually in an emergency department setting [ 12 , 22 ]. Although these may be helpful for clinical decision making, they are not useful from a prevention standpoint.…”