Interpretable machine learning model for comparing and validating three diagnostic criteria for bronchopulmonary dysplasia in predicting value of respiratory prognosis of preterm infants: a retrospective cohort study

Both the 2018 NICHD and 2019 NRN criteria for BPD show better and similar predictive values for respiratory adverse outcomes in preterm infants, and both are superior to the 2001 NICHD criteria. The top two factors affecting the respiratory prognosis of preterm infants are the severity of BPD and early invasive mechanical ventilation.

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Publication Details

Journal
Frontiers in Pediatrics
Published
2025-11-25
DOI
https://doi.org/10.3389/fped.2025.1678244
Primary Topic
Neonatal Respiratory Health Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Interpretable machine learning model for comparing and validating three diagnostic criteria for bronchopulmonary dysplasia in predicting value of respiratory prognosis of preterm infants: a retrospective cohort study

Xin Wang, Wenqing Kang, Caijun Wang, Yanmei Zhao et al.
Frontiers in Pediatrics
Neonatal Respiratory Health Research
article

Interpretable machine learning model for comparing and validating three diagnostic criteria for bronchopulmonary dysplasia in predicting value of respiratory prognosis of preterm infants: a retrospective cohort study

Xin Wang, Wenqing Kang, Caijun Wang, Yanmei Zhao, Qingcui Bu, Yingyuan Wang, Guiying Sun, Rui Li, Yanyan Wu
article en

Abstract

Both the 2018 NICHD and 2019 NRN criteria for BPD show better and similar predictive values for respiratory adverse outcomes in preterm infants, and both are superior to the 2001 NICHD criteria. The top two factors affecting the respiratory prognosis of preterm infants are the severity of BPD and early invasive mechanical ventilation.

Frontiers in PediatricsVol. 13
Zhengzhou Children's Hospital (CN)
Openalex Percentile: Top 46%
Neonatal Respiratory Health Research
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