Development and validation of a nomogram to predict bronchopulmonary dysplasia in very preterm, very low birth weight infants.

OBJECTIVES

This study aimed to identify early predictors of bronchopulmonary dysplasia (BPD) in very preterm, very low birth weight infants and to construct and externally validate a nomogram that quantifies individual BPD risk shortly after birth to guide proactive clinical management.

METHODS

We retrospectively analyzed 304 preterm infants admitted to our hospital between 2019-2024. The cohort comprised 113 infants diagnosed with BPD and 191 non-BPD controls. Clinical data, including maternal characteristics, neonatal parameters, and hematological indices measured at 14 days of postnatal age, were collected. Significant predictors of BPD were identified using logistic regression analysis and incorporated into a nomogram model for BPD risk assessment. The model's performance was externally validated using an independent cohort of 30 preterm infants admitted between January and June 2025.

RESULTS

Factor analysis identified nine key BPD predictors (gestational age, birth weight, hypertensive disorders, neonatal respiratory distress syndrome, patent ductus arteriosus, blood transfusion, duration of nasal continuous positive airway pressure therapy, mean platelet volume, and white blood cell count), which were used to develop a BPD risk nomogram. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.946 (95% CI: 0.927-0.966) for internal validation and 0.883 (95% CI: 0.750-0.989) for external validation, indicating a high discriminative ability.

CONCLUSION

The results of this study provide an important basis for the early identification and management of BPD in premature infants and have potential clinical application value, which is helpful in improving the prognosis of children and optimizing the allocation of medical resources.