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Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes

BACKGROUND: Perinatal complications are common burdens for neonates born from mother with pPROM. Physicians and parents sometimes need to make critical decisions about neonatal care with short- and long-term implications on infant’s health and families and it is important to predict severe neonatal...

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Autores principales: Zhuang, Lu, Li, Zhan-Kui, Zhu, Yuan-Fang, Ju, Rong, Hua, Shao-Dong, Yu, Chun-Zhi, Li, Xing, Zhang, Yan-Ping, Li, Lei, Yu, Yan, Zeng, Wen, Cui, Jie, Chen, Xin-Yu, Peng, Jing-Ya, Li, Ting, Feng, Zhi-Chun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252037/
https://www.ncbi.nlm.nih.gov/pubmed/35787798
http://dx.doi.org/10.1186/s12884-022-04855-0
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author Zhuang, Lu
Li, Zhan-Kui
Zhu, Yuan-Fang
Ju, Rong
Hua, Shao-Dong
Yu, Chun-Zhi
Li, Xing
Zhang, Yan-Ping
Li, Lei
Yu, Yan
Zeng, Wen
Cui, Jie
Chen, Xin-Yu
Peng, Jing-Ya
Li, Ting
Feng, Zhi-Chun
author_facet Zhuang, Lu
Li, Zhan-Kui
Zhu, Yuan-Fang
Ju, Rong
Hua, Shao-Dong
Yu, Chun-Zhi
Li, Xing
Zhang, Yan-Ping
Li, Lei
Yu, Yan
Zeng, Wen
Cui, Jie
Chen, Xin-Yu
Peng, Jing-Ya
Li, Ting
Feng, Zhi-Chun
author_sort Zhuang, Lu
collection PubMed
description BACKGROUND: Perinatal complications are common burdens for neonates born from mother with pPROM. Physicians and parents sometimes need to make critical decisions about neonatal care with short- and long-term implications on infant’s health and families and it is important to predict severe neonatal outcomes with high accuracy. METHODS: The study was based on our prospective study on 1001 preterm infants born from mother with pPROM from August 1, 2017, to March 31, 2018 in three hospitals in China. Multivariable logistic regression analysis was applied to build a predicting model incorporating obstetric and neonatal characteristics available within the first day of NICU admission. We used enhanced bootstrap resampling for internal validation. RESULTS: One thousand one-hundred pregnancies with PROM at preterm with a single fetus were included in our study. SNO was diagnosed in 180 (17.98%) neonates. On multivariate analysis of the primary cohort, independent factors for SNO were respiratory support on the first day,, surfactant on day 1, and birth weight, which were selected into the nomogram. The model displayed good discrimination with a C-index of 0.838 (95%CI, 0.802–0.874) and good calibration performance. High C-index value of 0.835 could still be reached in the internal validation and the calibration curve showed good agreement. Decision curve analysis showed if the threshold is > 15%, using our model would achieve higher net benefit than model with birthweight as the only one predictor. CONCLUSION: Variables available on the first day in NICU including respiratory support on the first day, the use of surfactant on the first day and birthweight could be used to predict the risk of SNO in infants born from mother with pPROM with good discrimination and calibration performance.
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spelling pubmed-92520372022-07-05 Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes Zhuang, Lu Li, Zhan-Kui Zhu, Yuan-Fang Ju, Rong Hua, Shao-Dong Yu, Chun-Zhi Li, Xing Zhang, Yan-Ping Li, Lei Yu, Yan Zeng, Wen Cui, Jie Chen, Xin-Yu Peng, Jing-Ya Li, Ting Feng, Zhi-Chun BMC Pregnancy Childbirth Research BACKGROUND: Perinatal complications are common burdens for neonates born from mother with pPROM. Physicians and parents sometimes need to make critical decisions about neonatal care with short- and long-term implications on infant’s health and families and it is important to predict severe neonatal outcomes with high accuracy. METHODS: The study was based on our prospective study on 1001 preterm infants born from mother with pPROM from August 1, 2017, to March 31, 2018 in three hospitals in China. Multivariable logistic regression analysis was applied to build a predicting model incorporating obstetric and neonatal characteristics available within the first day of NICU admission. We used enhanced bootstrap resampling for internal validation. RESULTS: One thousand one-hundred pregnancies with PROM at preterm with a single fetus were included in our study. SNO was diagnosed in 180 (17.98%) neonates. On multivariate analysis of the primary cohort, independent factors for SNO were respiratory support on the first day,, surfactant on day 1, and birth weight, which were selected into the nomogram. The model displayed good discrimination with a C-index of 0.838 (95%CI, 0.802–0.874) and good calibration performance. High C-index value of 0.835 could still be reached in the internal validation and the calibration curve showed good agreement. Decision curve analysis showed if the threshold is > 15%, using our model would achieve higher net benefit than model with birthweight as the only one predictor. CONCLUSION: Variables available on the first day in NICU including respiratory support on the first day, the use of surfactant on the first day and birthweight could be used to predict the risk of SNO in infants born from mother with pPROM with good discrimination and calibration performance. BioMed Central 2022-07-04 /pmc/articles/PMC9252037/ /pubmed/35787798 http://dx.doi.org/10.1186/s12884-022-04855-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhuang, Lu
Li, Zhan-Kui
Zhu, Yuan-Fang
Ju, Rong
Hua, Shao-Dong
Yu, Chun-Zhi
Li, Xing
Zhang, Yan-Ping
Li, Lei
Yu, Yan
Zeng, Wen
Cui, Jie
Chen, Xin-Yu
Peng, Jing-Ya
Li, Ting
Feng, Zhi-Chun
Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title_full Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title_fullStr Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title_full_unstemmed Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title_short Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
title_sort predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252037/
https://www.ncbi.nlm.nih.gov/pubmed/35787798
http://dx.doi.org/10.1186/s12884-022-04855-0
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