Multidimensional Phenotype Classification in Grade 3 Bronchopulmonary Dysplasia

Description

Bronchopulmonary Dysplasia (BPD), or infant chronic lung disease, is the most consequential morbidity of prematurity. It affects >50% of extremely preterm infants ($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.