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Bronchopulmonary dysplasia prediction models: a systematic review and meta-analysis with validation

Kwok, T’ng Chang; Batey, Natalie; Luu, Ka Ling; Prayle, Andrew; Sharkey, Don

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Clinical Research Fellow in Neonatal Medicine

Natalie Batey

Ka Ling Luu

Andrew Prayle

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Professor of Neonatal Medicine and Technologies


Abstract: Prediction models could identify infants at the greatest risk of bronchopulmonary dysplasia (BPD) and allow targeted preventative strategies. We performed a systematic review and meta-analysis with external validation of identified models. Studies using predictors available before day 14 of life to predict BPD in very preterm infants were included. Two reviewers assessed 7628 studies for eligibility. Meta-analysis of externally validated models was followed by validation using 62,864 very preterm infants in England and Wales. A total of 64 studies using 53 prediction models were included totalling 274,407 infants (range 32–156,587/study). In all, 35 (55%) studies predated 2010; 39 (61%) were single-centre studies. A total of 97% of studies had a high risk of bias, especially in the analysis domain. Following meta-analysis of 22 BPD and 11 BPD/death composite externally validated models, Laughon’s day one model was the most promising in predicting BPD and death (C-statistic 0.76 (95% CI 0.70–0.81) and good calibration). Six models were externally validated in our cohort with C-statistics between 0.70 and 0.90 but with poor calibration. Few BPD prediction models were developed with contemporary populations, underwent external validation, or had calibration and impact analyses. Contemporary, validated, and dynamic prediction models are needed for targeted preventative strategies. Impact: This review aims to provide a comprehensive assessment of all BPD prediction models developed to address the uncertainty of which model is sufficiently valid and generalisable for use in clinical practice and research.Published BPD prediction models are mostly outdated, single centre and lack external validation.Laughon’s 2011 model is the most promising but more robust models, using contemporary data with external validation are needed to support better treatments.

Journal Article Type Article
Acceptance Date Dec 14, 2022
Online Publication Date Jan 9, 2023
Publication Date 2023-07
Deposit Date Jan 4, 2023
Publicly Available Date Jan 10, 2023
Journal Pediatric Research
Print ISSN 0031-3998
Electronic ISSN 1530-0447
Publisher Springer Science and Business Media LLC
Peer Reviewed Peer Reviewed
Volume 94
Pages 43–54
Keywords Pediatrics, Perinatology and Child Health
Public URL
Publisher URL
Additional Information Received: 20 September 2022; Revised: 6 December 2022; Accepted: 14 December 2022; First Online: 9 January 2023; : The authors declare no competing interests.


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