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DOI 10.1038/s41390-021-01779-x

Медицинская библиотека · Pediatric research · 2021

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

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: 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.. Pediatric research. 2021. DOI: 10.1038/s41390-021-01779-x.

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