Uncovering Hidden Phenotypes of Bronchopulmonary Dysplasia: A Latent Class Analysis of Preterm Infants in a Middle-Income Setting

Background: Bronchopulmonary dysplasia (BPD) is a heterogeneous syndrome encompassing multiple biological and structural phenotypes. This study aimed to (1) identify latent phenotypes of in-hospital respiratory trajectories among preterm infants born at ≤ 32 weeks, and (2) compare these data-driven subgroups with traditional BPD severity classifications in predicting in-hospital mortality. Methods: We conducted a retrospective cohort of preterm infants born at ≤ 32 weeks' gestation and admitted to a tertiary neonatal unit in Medellín, Colombia (2021–2023). Infants with major malformations or genetic syndromes were excluded. Clinical and respiratory variables were analyzed using latent class analysis (LCA) to identify subgroups based on markers of prematurity, oxygen dependency, and respiratory support. Results: Among 236 infants, a three-class model best described the data (AIC = 12,002; BIC = 12,440; entropy = 0.83). Class 1 (64%) comprised more mature infants with mild respiratory disease and 5% mortality. Class 2 (27%) showed prolonged oxygen therapy and intermediate outcomes. Class 3 (9%), composed mainly of extremely preterm infants, exhibited severe respiratory failure and 88% mortality. The classes reflected a progressive gradient of respiratory severity and mortality. Conclusions: LCA revealed three distinct BPD phenotypes with increasing respiratory severity and mortality, paralleling biological models of parenchymal, interstitial, and vascular injury. This approach supports data-driven phenotyping as a foundation for precision neonatal care.