Neurodevelopmental Outcomes of Moderate-to-Late Preterm Infants

Nam, Yun Sung; Heo, Ju Sun; Byeon, Jung Hye; Lee, Eun Hee

doi:10.5385/nm.2020.27.4.159

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…These prognostic trends have led studies to improve our understanding of neurodevelopmental disorders in EP infants and identify the most vulnerable preterm infants 7 , 8 . However, increasing evidence of long-term neurodevelopmental delays in V-LP infants, and previous findings suggest that neurodevelopmental outcomes of EP and V-LP differ 9 – 11 . The developing brain may be affected differently, depending on the level of prematurity 12 .…”

Section: Introductionmentioning

confidence: 91%

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

Jang,

Ham,

Kasani

et al. 2024

Sci Rep

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The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

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Section: Introductionmentioning

confidence: 91%

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

Jang,

Ham,

Kasani

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Segundo a Organização Mundial da Saúde, prematuros serão os recém-nascidos com uma idade gestacional menor do que 37 semanas de gestação e prematuros extremos serão aqueles com menos de 28 semanas. Neste sentido, quanto menor for a idade gestacional, maior serão os riscos e as necessidade de cuidados intensivos à saúde do recémnascido (Sung, Sun, Hye & Hee, 2020).…”

Section: Introductionunclassified

Prematuridade: o acolhimento profissional em Unidade de Terapia Intensiva Neonatal

Pinheiro

Cardoso

Vargas

et al. 2021

RSD

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A prematuridade caracteriza-se como uma condição de risco para a manutenção da vida do recém-nascido, pois a estrutura e funcionalidade dos órgãos, a depender da idade gestacional em que se inicia a vida extrauterina, podem não ser compatíveis com essa. Neste sentido, quanto menor for a idade gestacional, maior serão os riscos e as necessidade de cuidados intensivos. Elaborou-se este estudo com o objetivo de fortalecer a disseminação do acolhimento profissional para mães de prematuros internados em Unidade de Terapia Intensiva Neonatal. O método adotado foi um relato de experiência de profissionais da saúde sobre o acolhimento profissional para mães de recém-nascidos prematuros assistido em UTIN durante o período de setembro de 2019 a outubro de 2020. As informações foram capturadas a partir da escuta e do diálogo com cinco mães. Os resultados encontrados expressam o medo da perda e a insegurança quanto ao desenvolvimento do recém-nascidos e destacam a enfermagem como fonte de segurança, esperança e apoio às mães, a partir do acolhimento. A equipe de enfermagem ganhou destaque nas ações de acolhimento, pois esteve presente e atuante nos diferentes níveis e unidades de cuidado com foco na humanização da atenção. Comunicou-se e primou pela execução dos procedimentos de assistência à saúde e controle e prevenção do coronavírus.

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Predicting two-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity: A machine learning approach

Jang,

Ham,

Kasani

et al. 2024

Preprint

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Determine brain structural networks in extremely preterm (EP; <28 weeks), very-to-late preterm (V-LP; ≥28 and < 37 weeks), and all preterm infants at term-equivalent age. Predict 2-year neurodevelopmental scores using multimodal predictors. Prospective cross-sectional study with MRI and diffusion MRI on 61 EP and 131 V-LP infants. Constructed a multimodal feature set through volumetric and structural network analysis. Linear and nonlinear machine learning models used for predicting Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive power and feature importance. Prediction models, incorporating local connectivity features, demonstrated high predictive performance for cognitive scores in preterm (RMSE 13.352; variance explained 17%) and V-LP (RMSE 11.205; variance explained 17%) infants. For motor scores, models with local connectivity features had the highest predictive performance for EP (RMSE 11.363; variance explained 15%). A model with only local connectivity features showed high predictive performance for language scores in preterm infants (RMSE 11.792; variance explained 15%). BSID-III prediction performance and feature importance varied across preterm groups, emphasizing the efficacy of multimodal feature sets with local connectivity. Leveraging machine learning in this context enhances our understanding of microstructural alterations and their link to neurodevelopmental outcomes, facilitating risk stratification.

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Neurodevelopmental Outcomes of Moderate-to-Late Preterm Infants

Cited by 5 publications

References 31 publications

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

Prematuridade: o acolhimento profissional em Unidade de Terapia Intensiva Neonatal

Predicting two-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity: A machine learning approach

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