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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...

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Autores principales: Liu, Qin, Tang, Zaixiang, Li, Huijun, Li, Yongfu, Tian, Qiuyan, Yang, Zuming, Miao, Po, Yang, Xiaofeng, Li, Mei, Xu, Lixiao, Feng, Xing, Ding, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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