Bronchopulmonary Dysplasia (BPD), or infant chronic lung disease, is the most
consequential morbidity of prematurity. It affects >50% of extremely preterm infants
(<30wk gestation) and can incur >$1 million in costs per child. Among infants who develop
grade 3 BPD (most severe grade, defined as invasive ventilation at 36 weeks'
postmenstrual age), nearly 80% suffer life-long respiratory impairment and >60% suffer
severe developmental disability. Rates of grade 3 BPD are increasing and no proven
therapies treat this disease. A key contributor to these gaps is the nearly singular
reliance on the prescribed respiratory support to define BPD severity, select therapies,
and assess prognosis. This subjective diagnostic approach masks heterogeneity in clinical
presentation, treatment responsiveness, and outcomes. In other heterogenous lung diseases
such as chronic obstructive pulmonary disease, cystic fibrosis, and asthma,
evidence-based phenotyping (identification of patient subgroups based on shared
characteristics) objectively classifies disease sub-types, improves patient counseling,
promotes discovery of novel pathological mechanisms, and leads to more effective,
phenotype-targeted therapies. The central hypothesis of the present study is that deep,
multidimensional phenotyping in grade 3 BPD is feasible with existing diagnostic
technologies, will reliably characterize disease heterogeneity, and will improve outcome
prediction. Confirmation of this hypothesis holds promise to promote a frameshift towards
objective diagnostic approaches and first-of-their-kind phenotype-specific trials in
infants with BPD.
Existing preliminary data support the feasibility of phenotyping in grade 3 BPD and
suggest newer diagnostic techniques may improve disease characterization. Using data from
lung computed tomography scan, cardiac echo, and bronchoscopy, researchers showed that
preterm infants with grade 3 BPD can be classified into phenotypes based on the presence
or absence of severe parenchymal lung disease, abnormal large airways, and pulmonary
arterial hypertension. This classification scheme correlated with pre-discharge outcomes
and suggested possible phenotype-specific therapies. Recent discoveries indicate that
serial quantitative cardiopulmonary imaging and evaluation of mechanistic contributors to
BPD including lung inflammation, gastroesophageal reflux, recurrent hypoxemia, and lung
microbial dysbiosis may improve disease phenotyping and prediction of childhood
neurodevelopmental and respiratory outcomes. This study builds on this information and
uses multidimensional imaging, biological, and clinical data plus robust statistical
techniques to propose an objective phenotype classification system for grade 3 BPD.
Enrolled infants will undergo baseline quantitative chest computed tomography with
angiography (CTA), cardiac echocardiography, bronchoscopy with lavage, 24-hour esophageal
pH-impedance testing, pulmonary mechanics testing, oximetry, and complete medical record
review at enrollment. Repeat diagnostic testing will be performed 6-8wk later and
cardiopulmonary monitoring and outcome data collected until discharge. These data will be
used to empirically define phenotypes and assess phenotype stability. Enrolled
participants will undergo validated neurodevelopmental and respiratory assessments
through 2 years' corrected age. The diagnostic performance the empirically defined
phenotype classification system for predicting 2 year outcomes will be determined.