Objective Existing predictive models for bronchopulmonary dysplasia (BPD) often lack external validation, limiting their clinical use. This study aimed to externally validate recent BPD prediction models using baseline variables, in a population-based cohort.Design This was an external validation study conducted on data collected from 2014 to 2021.Setting This was a retrospective, multicentre, population-level cohort with prospectively collected data.Participants Extremely low gestational age neonates recorded in the SwissNeoNet registry across all nine level III neonatal care units in Switzerland (n=1748) were included.Interventions Recent BPD prediction models estimating the risk of BPD or death at 36 weeks postmenstrual age, based on predictors available within the first 24 hours of life.Main Outcome Measures The primary outcome was survival without BPD. A systematic literature search identified five eligible models, which were externally validated and recalibrated for the Swiss cohort. The most performant model was further optimised to improve local applicability.Results Among 693 screened studies, five models based solely on perinatal variables were included. Without recalibration, models showed fair discrimination (area under the curve (AUC) 0.70-0.76) but variable calibration (observed/expected (O/E) 0.58-0.80). After recalibration, AUCs ranged from 0.69 to 0.76, and calibration improved (O/E 0.58-1.61). The optimised version of the best-performing model demonstrated improved calibration (O/E 1.03) and was validated in the Swiss population.Conclusion By comparing and externally validating existing BPD prediction models, we propose an optimised model using baseline variables at birth, enhancing its applicability to both the Swiss population and similar clinical contexts.
Bleeker et al. (Fri,) studied this question.