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

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.