Evidence map›Paper›PMID 36624282›Full record

SynthesisPediatric research2023

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

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

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
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  17. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

T'ng Chang KwokCentre for Perinatal Research, School of Medicine, Queen's Medical Centre, University of Nottingham, E Floor, East Block, Nottingham, NG7 2UH, UK.
Natalie BateyNottingham Neonatal Service, Queen's Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, NG7 2UH, UK.
Ka Ling LuuNottingham Maternity Service, Queen's Medical Centre, Nottingham University Hospitals NHS Trust, Nottingham, NG7 2UH, UK.
Andrew PrayleCentre for Perinatal Research, School of Medicine, Queen's Medical Centre, University of Nottingham, E Floor, East Block, Nottingham, NG7 2UH, UK.
Don SharkeyCentre for Perinatal Research, School of Medicine, Queen's Medical Centre, University of Nottingham, E Floor, East Block, Nottingham, NG7 2UH, UK. Don.sharkey@nottingham.ac.uk.ORCID 0000-0002-4989-8697
Nottingham University Hospitals NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Bronchopulmonary DysplasiaInfant, Premature, DiseasesEnglandHumansInfantInfant, NewbornInfant, PrematureInfant, Very Low Birth Weight

Identifiers

PMID36624282
PMCPMC10356605
OpenAlexW4313894455

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.