Tracheostomy prediction model in neonatal bronchopulmonary dysplasia via lung and airway MRI.

Rationale: Clinical management of neonatal bronchopulmonary dysplasia (BPD) is often imprecise and can vary widely between different institutions and providers, due to limited objective measurements of disease pathology severity. There is critical need to improve guidance on the application and timing of interventional treatments, such as tracheostomy.
Objectives: To generate an imaging-based clinical tool for early identification of those patients with BPD who are likely to require later tracheostomy and long-term mechanical ventilation.
Methods: We conducted a prospective cohort study of n = 61 infants (55 BPD, 6 preterm non-BPD). Magnetic resonance imaging (MRI) scores of lung parenchymal disease were used to create a binomial logistic regression model for predicting tracheostomy requirement. This model was further investigated using clinical variables and MRI-quantified tracheomalacia (TM).
Measurements and Main Results: A model for predicting tracheostomy requirement was created using MRI parenchymal score. This model had 89% accuracy, 100% positive predictive value (PPV), and 85% negative predictive value (NPV), compared with 84%, 60%, and 83%, respectively, when using only relevant clinical variables. In a subset of patients with airway MRI (n = 36), a model including lung and TM measurements had 83% accuracy, 92% PPV, and 78% NPV.
Conclusions: MRI-based measurements of parenchymal disease and TM can be used to predict need for tracheostomy in infants with BPD, more accurately than clinical factors alone. This prediction model has strong potential as a clinical tool for physicians and families for early determination of tracheostomy requirement.
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