BMC infectious diseases · 2025
Clinical risk factors and a predictive nomogram for mechanical ventilation in pediatric patients with infection-related plastic bronchitis.
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Peng Hongyan, Yang Yiyu, Chen Feiyan, Zhang Jianhui, Liang Zhuoxin, Tao Jianping, Dang Run, Hong Jie, Zhang Chunmin, Wei Xiaoxing, Zuo Yunlong, Feng Lin
Аннотация
BACKGROUND: Plastic bronchitis (PB) in children may cause respiratory failure requiring mechanical ventilation (MV), but systematic risk assessments are lacking. This study aimed to identify clinical risk factors for MV in infection-related PB and to develop a predictive nomogram for individualized risk stratification.
METHODS: In this retrospective cohort study, pediatric patients diagnosed with infection-related PB at our center between August 2019 and August 2025 were included. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for MV. A predictive model and nomogram were developed, with discrimination evaluated by area under the receiver operating characteristic curve (AUC) and calibration assessed using bootstrap resampling.
RESULTS: A total of 103 pediatric patients were included in the study, comprising 78 with single-pathogen infections and 25 with mixed infections. Among them, 20 (19.4%) required MV. Multivariate analysis identified younger age (OR = 0.93, 95%CI: 0.88-0.97), elevated PaCO₂ (OR = 5.44, 95%CI: 2.10-21.1), and more extensive lobar involvement (OR = 6.53, 95%CI: 1.85-32.4) as independent risk factors. The model based on age and PaCO₂ achieved an AUC of 0.922; adding lobar involvement slightly increased the AUC to 0.954 without statistical significance (p = 0.118). Nomogram-predicted probabilities closely matched observed outcomes (mean absolute error = 0.023).
CONCLUSIONS: Age, PaCO₂, and lobar involvement were independent predictors of MV in pediatric infection-related PB. The model based on age and PaCO₂ demonstrated high discrimination and reliable calibration, and the derived nomogram serves as a practical tool for early risk assessment and individualized management.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: In this retrospective cohort study, pediatric patients diagnosed with infection-related PB at our center between August 2019 and August 2025 were included. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for MV. A predictive model and nomogram were developed, with discrimination evaluated by area under the receiver operating characteristic curve (AUC) and calibration assessed using bootstrap resampling.
RESULTS: A total of 103 pediatric patients were included in the study, comprising 78 with single-pathogen infections and 25 with mixed infections. Among them, 20 (19.4%) required MV. Multivariate analysis identified younger age (OR = 0.93, 95%CI: 0.88-0.97), elevated PaCO₂ (OR = 5.44, 95%CI: 2.10-21.1), and more extensive lobar involvement (OR = 6.53, 95%CI: 1.85-32.4) as independent risk factors. The model based on age and PaCO₂ achieved an AUC of 0.922; adding lobar involvement slightly increased the AUC to 0.954 without statistical significance (p = 0.118). Nomogram-predicted probabilities closely matched observed outcomes (mean absolute error = 0.023).
CONCLUSIONS: Age, PaCO₂, and lobar involvement were independent predictors of MV in pediatric infection-related PB. The model based on age and PaCO₂ demonstrated high discrimination and reliable calibration, and the derived nomogram serves as a practical tool for early risk assessment and individualized management.
CLINICAL TRIAL NUMBER: Not applicable.