Cargando…
Language function following preterm birth: prediction using machine learning
BACKGROUND: Preterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm. METHODS: We analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503721/ https://www.ncbi.nlm.nih.gov/pubmed/34635792 http://dx.doi.org/10.1038/s41390-021-01779-x |
_version_ | 1784581189697273856 |
---|---|
author | Valavani, Evdoxia Blesa, Manuel Galdi, Paola Sullivan, Gemma Dean, Bethan Cruickshank, Hilary Sitko-Rudnicka, Magdalena Bastin, Mark E. Chin, Richard F. M. MacIntyre, Donald J. Fletcher-Watson, Sue Boardman, James P. Tsanas, Athanasios |
author_facet | Valavani, Evdoxia Blesa, Manuel Galdi, Paola Sullivan, Gemma Dean, Bethan Cruickshank, Hilary Sitko-Rudnicka, Magdalena Bastin, Mark E. Chin, Richard F. M. MacIntyre, Donald J. Fletcher-Watson, Sue Boardman, James P. Tsanas, Athanasios |
author_sort | Valavani, Evdoxia |
collection | PubMed |
description | BACKGROUND: Preterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm. METHODS: We analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-equivalent age and language assessment at 2 years CGA using the Bayley-III. Feature selection and a random forests classifier were used to differentiate typical versus delayed (Bayley-III language composite score <85) language development. RESULTS: The model achieved balanced accuracy: 91%, sensitivity: 86%, and specificity: 96%. The probability of language delay at 2 years CGA is increased with: increasing values of peak width of skeletonized fractional anisotropy (PSFA), radial diffusivity (PSRD), and axial diffusivity (PSAD) derived from dMRI; among twins; and after an incomplete course of, or no exposure to, antenatal corticosteroids. Female sex and breastfeeding during the neonatal period reduced the risk of language delay. CONCLUSIONS: The combination of perinatal clinical information and MRI features leads to accurate prediction of preterm infants who are likely to develop language deficits in early childhood. This model could potentially enable stratification of preterm children at risk of language dysfunction who may benefit from targeted early interventions. IMPACT: A combination of clinical perinatal factors and neonatal DTI measures of white matter microstructure leads to accurate prediction of language outcome at 2 years corrected gestational age following preterm birth. A model that comprises clinical and MRI features that has potential to be scalable across centres. It offers a basis for enhancing the power and generalizability of diagnostic and prognostic studies of neurodevelopmental disorders associated with language impairment. Early identification of infants who are at risk of language delay, facilitating targeted early interventions and support services, which could improve the quality of life for children born preterm. |
format | Online Article Text |
id | pubmed-8503721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-85037212021-10-12 Language function following preterm birth: prediction using machine learning Valavani, Evdoxia Blesa, Manuel Galdi, Paola Sullivan, Gemma Dean, Bethan Cruickshank, Hilary Sitko-Rudnicka, Magdalena Bastin, Mark E. Chin, Richard F. M. MacIntyre, Donald J. Fletcher-Watson, Sue Boardman, James P. Tsanas, Athanasios Pediatr Res Clinical Research Article BACKGROUND: Preterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm. METHODS: We analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-equivalent age and language assessment at 2 years CGA using the Bayley-III. Feature selection and a random forests classifier were used to differentiate typical versus delayed (Bayley-III language composite score <85) language development. RESULTS: The model achieved balanced accuracy: 91%, sensitivity: 86%, and specificity: 96%. The probability of language delay at 2 years CGA is increased with: increasing values of peak width of skeletonized fractional anisotropy (PSFA), radial diffusivity (PSRD), and axial diffusivity (PSAD) derived from dMRI; among twins; and after an incomplete course of, or no exposure to, antenatal corticosteroids. Female sex and breastfeeding during the neonatal period reduced the risk of language delay. CONCLUSIONS: The combination of perinatal clinical information and MRI features leads to accurate prediction of preterm infants who are likely to develop language deficits in early childhood. This model could potentially enable stratification of preterm children at risk of language dysfunction who may benefit from targeted early interventions. IMPACT: A combination of clinical perinatal factors and neonatal DTI measures of white matter microstructure leads to accurate prediction of language outcome at 2 years corrected gestational age following preterm birth. A model that comprises clinical and MRI features that has potential to be scalable across centres. It offers a basis for enhancing the power and generalizability of diagnostic and prognostic studies of neurodevelopmental disorders associated with language impairment. Early identification of infants who are at risk of language delay, facilitating targeted early interventions and support services, which could improve the quality of life for children born preterm. Nature Publishing Group US 2021-10-11 2022 /pmc/articles/PMC8503721/ /pubmed/34635792 http://dx.doi.org/10.1038/s41390-021-01779-x Text en © Crown 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Clinical Research Article Valavani, Evdoxia Blesa, Manuel Galdi, Paola Sullivan, Gemma Dean, Bethan Cruickshank, Hilary Sitko-Rudnicka, Magdalena Bastin, Mark E. Chin, Richard F. M. MacIntyre, Donald J. Fletcher-Watson, Sue Boardman, James P. Tsanas, Athanasios Language function following preterm birth: prediction using machine learning |
title | Language function following preterm birth: prediction using machine learning |
title_full | Language function following preterm birth: prediction using machine learning |
title_fullStr | Language function following preterm birth: prediction using machine learning |
title_full_unstemmed | Language function following preterm birth: prediction using machine learning |
title_short | Language function following preterm birth: prediction using machine learning |
title_sort | language function following preterm birth: prediction using machine learning |
topic | Clinical Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503721/ https://www.ncbi.nlm.nih.gov/pubmed/34635792 http://dx.doi.org/10.1038/s41390-021-01779-x |
work_keys_str_mv | AT valavanievdoxia languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT blesamanuel languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT galdipaola languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT sullivangemma languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT deanbethan languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT cruickshankhilary languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT sitkorudnickamagdalena languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT bastinmarke languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT chinrichardfm languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT macintyredonaldj languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT fletcherwatsonsue languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT boardmanjamesp languagefunctionfollowingpretermbirthpredictionusingmachinelearning AT tsanasathanasios languagefunctionfollowingpretermbirthpredictionusingmachinelearning |