Abstract
Background: There are few prognostic indicators of need for tracheostomy in preterm infants with chronic lung disease (bronchopulmonary dysplasia, BPD).
Aims: To develop a clinically-useful tracheostomy prediction model in neonatal BPD using quantitative biomarkers from respiratory MRI.
Methods: Infants with and without BPD (N=61) underwent 3D lung and airway MRI (0.7 mm3) near term-age. Lung disease scores (0-14 points) were used to create a binomial logistic regression model (⅔*N) to determine likelihood of tracheostomy (yes/no tracheostomy outcome assigned by 75% probability threshold), with validation in a separate cohort (⅓*N). A sub-cohort model also included MRI-quantified tracheomalacia severity (n=36).
Results: The model correctly classified 95% of the validation cohort. The full-cohort model had 89% accuracy, 100% positive predictive value, and 85% negative predictive value. The lung+airway model values were 83%, 92%, and 78%, respectively.
Conclusions: Quantitative respiratory MRI can predict need for tracheostomy in neonatal BPD with high sensitivity and accuracy, providing an objective tool for clinical decision-making.
Footnotes
Cite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4789.
This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2020