ArticleBMC infectious diseases2023
Development and validation of a nomogram for predicting severe respiratory syncytial virus-associated bronchiolitis.
Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 3 citations in OpenAlex.
- Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study.European journal of pediatrics · 2026Article
- Global trends and advances in pediatric bronchiolitis treatment in the last two decades: a bibliometric analysis.Journal of thoracic disease · 2026Article
- Early prediction of severe RSV-associated ALRTI in Asian pediatric patients: a simple nomogram.BMC pediatrics · 2025Article
- Personalized Infant Risk Prediction for Severe Respiratory Syncytial Virus Lower Respiratory Tract Infection Requiring Intensive Care Unit Admission.Open forum infectious diseases · 2024Article
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8 authors at 3 institutions in 1 country.
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Abstract
backgroundRespiratory 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.
methodsA 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).
resultsThere 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.
conclusionA 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.
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