Validation of a new predictive model to improve risk stratifIcation in bronchopulmonary dysplasia (2020)
We need a better risk stratifcation system for the increasing number of survivors of extreme prematurity sufering the most severe forms of bronchopulmonary dysplasia (BPD). However, there is still a paucity of studies providing scientifc evidence to guide future updates of BPD severity defnitions. Our goal was to validate a new predictive model for BPD severity that incorporates respiratory assessments beyond 36 weeks postmenstrual age (PMA). We hypothesized that this approach improves BPD risk assessment, particularly in extremely premature infants. This is a longitudinal cohort of premature infants (≤32 weeks PMA, n=188; Washington D.C). We performed receiver operating characteristic analysis to defne optimal BPD severity levels using the duration of supplementary O2 as predictor and respiratory hospitalization after discharge as outcome. Internal validation included lung X-ray imaging and phenotypical characterization of BPD severity levels. External validation was conducted in an independent longitudinal cohort of premature infants (≤36 weeks PMA, n=130; Bogota). We found that incorporating the total number of days requiring O2 (without restricting at 36 weeks PMA) improved the prediction of respiratory outcomes according to BPD severity. In addition, we defned a new severity category (level IV) with prolonged exposure to supplemental O2 (≥120 days) that has the highest risk of respiratory hospitalizations after discharge. We confrmed these fndings in our validation cohort using ambulatory determination of O2 requirements. In conclusion, a new predictive model for BPD severity that incorporates respiratory assessments beyond 36 weeks improves risk stratifcation and should be considered when updating current BPD severity defnitions.