Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

Michelle Romijn; Paula Dhiman; Martijn J J Finken; Anton H van Kaam; Trixie A Katz; Joost Rotteveel; Ewoud Schuit; Gary S Collins; Wes Onland; Heloise Torchin

doi:10.1016/j.jpeds.2023.01.024

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

Michelle Romijn^*, Paula Dhiman, Martijn J J Finken, Anton H van Kaam, Trixie A Katz, Joost Rotteveel, Ewoud Schuit, Gary S Collins, Wes Onland, Heloise Torchin

^*Corresponding author for this work

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Abstract

OBJECTIVE: To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.

STUDY DESIGN: Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).

RESULTS: Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.

CONCLUSIONS: Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.

Original language	English
Article number	113370
Number of pages	35
Journal	The Journal of Pediatrics
Volume	258
Early online date	13 Apr 2023
DOIs	https://doi.org/10.1016/j.jpeds.2023.01.024
Publication status	Published - Jul 2023

Keywords

chronic lung disease
neonatology
prediction
premature

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1-s2.0-S0022347623000513-mainFinal published version, 1.17 MBLicence: CC BY

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@article{df4bb7968e454ed6b27a1bdf7654c146,

title = "Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis",

abstract = "OBJECTIVE: To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.STUDY DESIGN: Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).RESULTS: Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.CONCLUSIONS: Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.",

keywords = "chronic lung disease, neonatology, prediction, premature",

author = "Michelle Romijn and Paula Dhiman and Finken, {Martijn J J} and {van Kaam}, {Anton H} and Katz, {Trixie A} and Joost Rotteveel and Ewoud Schuit and Collins, {Gary S} and Wes Onland and Heloise Torchin",

note = "Funding Information: M.R. was supported by the Amsterdam Reproduction & Development Research Institute . P.D. was supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Oxford. G.C. was supported by the NIHR Biomedical Research Centre , Oxford, and Cancer Research UK (program grant: C49297 / A27294 ). H.T. received grants from the French Society of Neonatology and from the French Society of Pediatrics . The authors declare no conflicts of interest. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = jul,

doi = "10.1016/j.jpeds.2023.01.024",

language = "English",

volume = "258",

journal = "The Journal of Pediatrics",

issn = "0022-3476",

publisher = "Mosby Inc.",

}

TY - JOUR

T1 - Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants

T2 - A Systematic Review and Meta-Analysis

AU - Romijn, Michelle

AU - Dhiman, Paula

AU - Finken, Martijn J J

AU - van Kaam, Anton H

AU - Katz, Trixie A

AU - Rotteveel, Joost

AU - Schuit, Ewoud

AU - Collins, Gary S

AU - Onland, Wes

AU - Torchin, Heloise

N1 - Funding Information: M.R. was supported by the Amsterdam Reproduction & Development Research Institute . P.D. was supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre, Oxford. G.C. was supported by the NIHR Biomedical Research Centre , Oxford, and Cancer Research UK (program grant: C49297 / A27294 ). H.T. received grants from the French Society of Neonatology and from the French Society of Pediatrics . The authors declare no conflicts of interest. Publisher Copyright: © 2023 The Author(s)

PY - 2023/7

Y1 - 2023/7

N2 - OBJECTIVE: To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.STUDY DESIGN: Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).RESULTS: Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.CONCLUSIONS: Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.

AB - OBJECTIVE: To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age.STUDY DESIGN: Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST).RESULTS: Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome.CONCLUSIONS: Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.

KW - chronic lung disease

KW - neonatology

KW - prediction

KW - premature

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U2 - 10.1016/j.jpeds.2023.01.024

DO - 10.1016/j.jpeds.2023.01.024

M3 - Article

C2 - 37059387

SN - 0022-3476

VL - 258

JO - The Journal of Pediatrics

JF - The Journal of Pediatrics

M1 - 113370

ER -

Prediction Models for Bronchopulmonary Dysplasia in Preterm Infants: A Systematic Review and Meta-Analysis

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