Rationale: Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current National Institute of Child Health and Human Development (NICHD)/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes.
Objectives: We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes.
Methods: A total of 42 neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD control subjects; 40 ± 3-wk postmenstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo time and gradient echo) in a neonatal ICU-sited, neonatal-sized 1.5 T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared with clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models, including clinical data and scores.
Measurements and main results: MRI scores significantly correlated with severities and predicted respiratory support at neonatal ICU discharge (P < 0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support.
Conclusions: Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this nonionizing technique can be implemented to phenotype disease, and has potential to serially assess efficacy of individualized therapies.
Keywords: bronchopulmonary dysplasia; magnetic resonance imaging; neonatal lung disease; outcome prediction modeling; prematurity.