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Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants

  • Authors:
    • Yan-Sha Pan
    • Lan Xiao
    • Wen-Bin Dong
    • Jia-Wen Dang
  • View Affiliations / Copyright

    Affiliations: Department of Pediatrics, Sichuan Clinical Research Center for Birth Defects, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
    Copyright: © Pan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 50
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    Published online on: December 9, 2025
       https://doi.org/10.3892/etm.2025.13045
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Abstract

Bronchopulmonary dysplasia (BPD) is a major morbidity in preterm infants, necessitating early risk assessment to guide interventions. The present study aimed to develop and internally validate a clinical prediction model for BPD. A total 120 preterm infants (<32 gestation weeks) admitted to a neonatal intensive care unit from January 2020 to December 2022 were retrospectively analyzed. Infants were retrospectively classified into BPD (n=34) and non-BPD (n=86) groups based on the 2018 National Institute of Child Health and Human Development criteria. Clinical variables, including maternal, neonatal, respiratory and comorbid factors, were assessed. Univariate and multivariate logistic regression identified independent predictors, which were used to construct a nomogram. Model performance was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve, a calibration curve and Hosmer-Lemeshow test. Internal validation was performed via bootstrapping. The results demonstrated that gestational age, birth weight, sepsis, patent ductus arteriosus and intraventricular hemorrhage were independent predictors of BPD. The model demonstrated good discrimination (AUC=0.918; 95% confidence interval, 0.866-0.971) and good calibration. The nomogram enabled individualized risk estimation, and internal validation confirmed model robustness. In conclusion, the proposed nomogram demonstrated strong discriminative power and clinical applicability for early BPD risk assessment. Future multicenter validation will help extend its generalizability across diverse neonatal populations.
View Figures

Figure 1

ROC curve for the BPD risk prediction
model. The ROC curve illustrates the diagnostic performance of the
prediction model for BPD. AUC=0.918 (95% CI, 0.866-0.971) indicated
the excellent discriminative ability of the model. BPD,
bronchopulmonary dysplasia; AUC, area under the curve; CI,
confidence interval; ROC, receiver operating characteristic; TPR,
true positive rate; FPR, false positive rate.

Figure 2

Calibration curve for the BPD risk
prediction model. The calibration curve compares the predicted
probabilities of BPD occurrence against the observed probabilities.
The apparent (blue) and bias-corrected (red) curves align closely
with the ideal line (gray dash), indicating a good calibration of
the model. BPD, bronchopulmonary dysplasia.

Figure 3

Nomogram for BPD risk prediction. The
nomogram visualizes the contribution of multiple predictors to the
risk of BPD. Predictors include gestational age, birth weight, sex,
antenatal steroids, respiratory support modes, sepsis, PDA, IVH and
NEC. The total points correspond to the predicted probability of
BPD. BPD, bronchopulmonary dysplasia CPAP, continuous positive
airway pressure; NIPPV; non-invasive positive pressure ventilation;
HFOV, high-frequency oscillatory ventilation; PDA, patent ductus
arteriosus; IVH, intraventricular hemorrhage; NEC, necrotizing
enterocolitis.
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Copy and paste a formatted citation
Spandidos Publications style
Pan Y, Xiao L, Dong W and Dang J: Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants. Exp Ther Med 31: 50, 2026.
APA
Pan, Y., Xiao, L., Dong, W., & Dang, J. (2026). Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants. Experimental and Therapeutic Medicine, 31, 50. https://doi.org/10.3892/etm.2025.13045
MLA
Pan, Y., Xiao, L., Dong, W., Dang, J."Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants". Experimental and Therapeutic Medicine 31.2 (2026): 50.
Chicago
Pan, Y., Xiao, L., Dong, W., Dang, J."Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants". Experimental and Therapeutic Medicine 31, no. 2 (2026): 50. https://doi.org/10.3892/etm.2025.13045
Copy and paste a formatted citation
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Spandidos Publications style
Pan Y, Xiao L, Dong W and Dang J: Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants. Exp Ther Med 31: 50, 2026.
APA
Pan, Y., Xiao, L., Dong, W., & Dang, J. (2026). Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants. Experimental and Therapeutic Medicine, 31, 50. https://doi.org/10.3892/etm.2025.13045
MLA
Pan, Y., Xiao, L., Dong, W., Dang, J."Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants". Experimental and Therapeutic Medicine 31.2 (2026): 50.
Chicago
Pan, Y., Xiao, L., Dong, W., Dang, J."Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants". Experimental and Therapeutic Medicine 31, no. 2 (2026): 50. https://doi.org/10.3892/etm.2025.13045
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