Dataset Information

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review.

ABSTRACT:

Objective

To provide an overview and critical appraisal of prediction models for bronchopulmonary dysplasia (BPD) in preterm infants.

Methods

We searched PubMed, Embase, and the Cochrane Library to identify relevant studies (up to November 2021). We included studies that reported prediction model development and/or validation of BPD in preterm infants born at ≤32 weeks and/or ≤1,500 g birth weight. We extracted the data independently based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). We assessed risk of bias and applicability independently using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

Twenty-one prediction models from 13 studies reporting on model development and 21 models from 10 studies reporting on external validation were included. Oxygen dependency at 36 weeks' postmenstrual age was the most frequently reported outcome in both development studies (71%) and validation studies (81%). The most frequently used predictors in the models were birth weight (67%), gestational age (62%), and sex (52%). Nearly all included studies had high risk of bias, most often due to inadequate analysis. Small sample sizes and insufficient event patients were common in both study types. Missing data were often not reported or were discarded. Most studies reported on the models' discrimination, while calibration was seldom assessed (development, 19%; validation, 10%). Internal validation was lacking in 69% of development studies.

Conclusion

The included studies had many methodological shortcomings. Future work should focus on following the recommended approaches for developing and validating BPD prediction models.

SUBMITTER: Peng HB

PROVIDER: S-EPMC9133667 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review.

Peng Hai-Bo HB Zhan Yuan-Li YL Chen You Y Jin Zhen-Chao ZC Liu Fang F Wang Bo B Yu Zhang-Bin ZB

Frontiers in pediatrics 20220512

<h4>Objective</h4>To provide an overview and critical appraisal of prediction models for bronchopulmonary dysplasia (BPD) in preterm infants.<h4>Methods</h4>We searched PubMed, Embase, and the Cochrane Library to identify relevant studies (up to November 2021). We included studies that reported prediction model development and/or validation of BPD in preterm infants born at ≤32 weeks and/or ≤1,500 g birth weight. We extracted the data independently based on the CHecklist for critical Appraisal a ...[more]

PMID: 35633976

Similar Datasets

Project description:ImportanceBronchopulmonary dysplasia (BPD), a chronic lung disease of prematurity, remains one of the major and most common complications of very preterm birth. Insight into factors associated with the pathogenesis of BPD is key to improving its prevention and treatment.ObjectiveTo perform a systematic review, meta-analysis, and metaregression of clinical studies exploring the association between chorioamnionitis (CA) and BPD in preterm infants.Data sourcesPubMed and Embase were searched without language restriction (last search, October 1, 2018). Key search terms included bronchopulmonary dysplasia, chorioamnionitis, and risk factors.Study selectionIncluded studies were peer-reviewed studies examining preterm (<37 weeks' gestation) or very low-birth-weight (<1500 g) infants and reporting primary data that could be used to measure the association between exposure to CA and the development of BPD.Data extraction and synthesisThe Meta-analysis of Observational Studies in Epidemiology (MOOSE) guideline was followed. Data were independently extracted by 2 researchers. A random-effects model was used to calculate odds ratios (ORs) and 95% CIs. Heterogeneity in effect size across studies was studied using multivariate, random-effects metaregression analysis.Main outcomes and measuresThe primary outcome was BPD, defined as supplemental oxygen requirement on postnatal day 28 (BPD28) or at the postmenstrual age of 36 weeks (BPD36). Covariates considered as potential confounders included differences between CA-exposed and CA-unexposed infants in gestational age, rates of respiratory distress syndrome (RDS), exposure to antenatal corticosteroids, and rates of early- and late-onset sepsis.ResultsA total of 3170 potentially relevant studies were found, of which 158 met the inclusion criteria (244?096 preterm infants, 20?971 CA cases, and 24?335 BPD cases). Meta-analysis showed that CA exposure was significantly associated with BPD28 (65 studies; OR, 2.32; 95% CI, 1.88-2.86; P?<?.001; heterogeneity: I2?=?84%; P?<?.001) and BPD36 (108 studies; OR, 1.29; 95% CI, 1.17-1.42; P?<?.001; heterogeneity: I2?=?63%; P?<?.001). The association between CA and BPD remained significant for both clinical and histologic CA. In addition, significant differences were found between CA-exposed and CA-unexposed infants in gestational age, birth weight, odds of being small for gestational age, exposure to antenatal corticosteroids, and early- and late-onset sepsis. Chorioamnionitis was not significantly associated with RDS (48 studies; OR, 1.10; 95% CI, 0.92-1.34; P?=?.24; heterogeneity: I2?=?90%; P?<?.001), but multivariate metaregression analysis with backward elimination revealed that a model combining the difference in gestational age and the odds of RDS was associated with 64% of the variance in the association between CA and BPD36 across studies.Conclusions and relevanceThe results of this study confirm that among preterm infants, exposure to CA is associated with a higher risk of developing BPD, but this association may be modulated by gestational age and risk of RDS.

Dataset Information

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review.

Objective

Methods

Results

Conclusion

Publications

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets