Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105246
Title: | A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks | Authors: | Li, Y Zhang, Z Mo, Y Wei, Q Jing, L Li, W Luo, M Zou, L Liu, X Meng, D Shi, Y |
Issue Date: | 2023 | Source: | Frontiers in neuroscience, 2023, v. 17, 1166800 | Abstract: | Introduction: Early identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method. Methods: Preterm infants with gestational age < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples. Results: In total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model. Conclusion: A support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population. |
Keywords: | Gestational age less than 32 weeks Neurodevelopmental impairment Prematurity Principal component analysis Support vector machine |
Publisher: | Frontiers Research Foundation | Journal: | Frontiers in neuroscience | ISSN: | 1662-453X | DOI: | 10.3389/fnins.2023.1166800 | Rights: | Copyright © 2023 Li, Zhang, Mo, Wei, Jing, Li, Luo, Zou, Liu, Meng and Shi. 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. The following publication Li Y, Zhang Z, Mo Y, Wei Q, Jing L, Li W, Luo M, Zou L, Liu X, Meng D and Shi Y (2023) A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks. Front. Neurosci. 17:1166800 is available at https://doi.org/10.3389/fnins.2023.1166800. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
fnins-17-1166800.pdf | 1.51 MB | Adobe PDF | View/Open |
Page views
15
Citations as of Jul 7, 2024
Downloads
4
Citations as of Jul 7, 2024
SCOPUSTM
Citations
2
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
2
Citations as of Jul 4, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.