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A nomogram model for predicting the risk factors of plastic bronchitis in children
Qiao Zhang1, Zhihua Zhao1, Ting Yang1
1Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents' Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, China.
Insights
A new nomogram predicts plastic bronchitis (PB) risk in children using seven clinical factors. This tool aids early identification and timely intervention for this critical respiratory condition.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Diagnostic Tools
Background:
- Plastic bronchitis (PB) is a severe pediatric respiratory condition necessitating prompt management.
- Early identification of children at high risk for PB is critical for effective, timely bronchoscopic intervention.
Purpose of the Study:
- To develop and validate a novel nomogram model for the early prediction of PB risk in pediatric patients.
- To identify key clinical parameters for predicting PB in children.
Main Methods:
- Retrospective analysis of clinical data from pediatric patients with respiratory conditions.
- Logistic regression analysis to identify independent risk factors for PB.
- Construction and validation of a predictive nomogram using training and independent validation sets.
Main Results:
- Seven independent predictors of PB were identified: elder age, cough duration, Mycoplasma pneumoniae infection, atelectasis, lung consolidation, pleural effusion, and pleurisy.
- The nomogram demonstrated excellent predictive ability with an AUC of 0.920 (training set) and 0.929 (validation set).
- The model showed good calibration, indicating reliability in predicting PB risk.
Conclusions:
- A practical, validated nomogram incorporating seven accessible clinical parameters for early PB risk assessment in children has been developed.
- This non-invasive tool can significantly aid clinical decision-making and facilitate timely intervention for pediatric plastic bronchitis.
Objective:
Plastic bronchitis (PB) in children is a critical condition requiring prompt intervention. Early identification of high-risk patients is crucial for timely bronchoscopic management. This study aimed to develop and validate a novel nomogram model for the early prediction of PB risk in pediatric patients.
Methods:
We conducted a retrospective analysis of clinical data from children with respiratory conditions. The cohort was divided into a training set (n = 326) and an independent validation set (n = 136). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for PB by comparing demographics, clinical symptoms, laboratory findings, and imaging features between groups. A predictive nomogram was subsequently constructed based on the results of the multivariate analysis.
Results:
Multivariate analysis identified seven independent predictors of PB: elder age, longer cough duration, mycoplasma pneumoniae infection, atelectasis, lung consolidation, pleural effusion, and pleurisy. The nomogram demonstrated excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.920 in the training set and 0.929 in the validation set. Good calibration was confirmed by the Hosmer-Lemeshow test (p = 0.545).
Conclusion:
We successfully developed and validated a practical nomogram incorporating seven readily available clinical parameters. This model serves as a reliable and non-invasive tool for the early assessment of PB risk in children, potentially facilitating timely clinical decision-making and intervention.
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