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A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study

BACKGROUND: Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS. M...

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Detalles Bibliográficos
Autores principales: Liu, Hui, Li, Jing, Guo, Jingyu, Shi, Yuan, Wang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241127/
https://www.ncbi.nlm.nih.gov/pubmed/35784441
http://dx.doi.org/10.1016/j.eclinm.2022.101523
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author Liu, Hui
Li, Jing
Guo, Jingyu
Shi, Yuan
Wang, Li
author_facet Liu, Hui
Li, Jing
Guo, Jingyu
Shi, Yuan
Wang, Li
author_sort Liu, Hui
collection PubMed
description BACKGROUND: Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS. METHODS: A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples. FINDINGS: Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324–0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133–0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083–0·909), hospital level (OR 2·479, 95% CI 1·260–4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563–0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer–Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. INTERPRETATION: A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS. FUNDING: No funding was associated with this study.
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spelling pubmed-92411272022-06-30 A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study Liu, Hui Li, Jing Guo, Jingyu Shi, Yuan Wang, Li eClinicalMedicine Articles BACKGROUND: Neonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS. METHODS: A prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples. FINDINGS: Multivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324–0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133–0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083–0·909), hospital level (OR 2·479, 95% CI 1·260–4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563–0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer–Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram. INTERPRETATION: A nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS. FUNDING: No funding was associated with this study. Elsevier 2022-06-25 /pmc/articles/PMC9241127/ /pubmed/35784441 http://dx.doi.org/10.1016/j.eclinm.2022.101523 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles
Liu, Hui
Li, Jing
Guo, Jingyu
Shi, Yuan
Wang, Li
A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title_full A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title_fullStr A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title_full_unstemmed A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title_short A prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: A retrospective study
title_sort prediction nomogram for neonatal acute respiratory distress syndrome in late-preterm infants and full-term infants: a retrospective study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9241127/
https://www.ncbi.nlm.nih.gov/pubmed/35784441
http://dx.doi.org/10.1016/j.eclinm.2022.101523
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