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The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators
Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phot...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592979/ https://www.ncbi.nlm.nih.gov/pubmed/36304529 http://dx.doi.org/10.3389/fped.2022.745423 |
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author | Liu, Qin Tang, Zaixiang Li, Huijun Li, Yongfu Tian, Qiuyan Yang, Zuming Miao, Po Yang, Xiaofeng Li, Mei Xu, Lixiao Feng, Xing Ding, Xin |
author_facet | Liu, Qin Tang, Zaixiang Li, Huijun Li, Yongfu Tian, Qiuyan Yang, Zuming Miao, Po Yang, Xiaofeng Li, Mei Xu, Lixiao Feng, Xing Ding, Xin |
author_sort | Liu, Qin |
collection | PubMed |
description | Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phototherapy failure. We retrospectively examined the medical records of 292 newborns with severe hyperbilirubinemia as the training cohort and another 52 neonates as the validation cohort. Logistic regression modeling was employed to create a predictive model with seven significant admission indicators, i.e., age, past medical history, presence of hemolysis, hemoglobin, neutrophil proportion, albumin (ALB), and total serum bilirubin (TSB). To validate the model, two other models with conventional indicators were created, one incorporating the admission indicators and phototherapy failure outcome and the other using TSB decrease after phototherapy failure as a variable and phototherapy outcome as an outcome indicator. The area under the curve (AUC) of the predictive model was 0.958 [95% confidence interval (CI): 0.924–0.993] and 0.961 (95% CI: 0.914–1.000) in the training and validation cohorts, respectively. Compared with the conventional models, the new model had better predictive power and greater value for clinical decision-making by providing a possibly earlier and more accurate prediction of phototherapy failure. More rapid clinical decision-making and interventions may potentially minimize occurrence of serious complications of severe neonatal hyperbilirubinemia. |
format | Online Article Text |
id | pubmed-9592979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95929792022-10-26 The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators Liu, Qin Tang, Zaixiang Li, Huijun Li, Yongfu Tian, Qiuyan Yang, Zuming Miao, Po Yang, Xiaofeng Li, Mei Xu, Lixiao Feng, Xing Ding, Xin Front Pediatr Pediatrics Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phototherapy failure. We retrospectively examined the medical records of 292 newborns with severe hyperbilirubinemia as the training cohort and another 52 neonates as the validation cohort. Logistic regression modeling was employed to create a predictive model with seven significant admission indicators, i.e., age, past medical history, presence of hemolysis, hemoglobin, neutrophil proportion, albumin (ALB), and total serum bilirubin (TSB). To validate the model, two other models with conventional indicators were created, one incorporating the admission indicators and phototherapy failure outcome and the other using TSB decrease after phototherapy failure as a variable and phototherapy outcome as an outcome indicator. The area under the curve (AUC) of the predictive model was 0.958 [95% confidence interval (CI): 0.924–0.993] and 0.961 (95% CI: 0.914–1.000) in the training and validation cohorts, respectively. Compared with the conventional models, the new model had better predictive power and greater value for clinical decision-making by providing a possibly earlier and more accurate prediction of phototherapy failure. More rapid clinical decision-making and interventions may potentially minimize occurrence of serious complications of severe neonatal hyperbilirubinemia. Frontiers Media S.A. 2022-10-11 /pmc/articles/PMC9592979/ /pubmed/36304529 http://dx.doi.org/10.3389/fped.2022.745423 Text en © 2022 Liu, Tang, Li, Li, Tian, Yang, Miao, Yang, Li, Xu, Feng and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pediatrics Liu, Qin Tang, Zaixiang Li, Huijun Li, Yongfu Tian, Qiuyan Yang, Zuming Miao, Po Yang, Xiaofeng Li, Mei Xu, Lixiao Feng, Xing Ding, Xin The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title | The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title_full | The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title_fullStr | The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title_full_unstemmed | The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title_short | The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
title_sort | development and validation of a predictive model for neonatal phototherapy outcome using admission indicators |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592979/ https://www.ncbi.nlm.nih.gov/pubmed/36304529 http://dx.doi.org/10.3389/fped.2022.745423 |
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