Statistical learning to identify and characterise neurodevelopmental outcomes at 2 years in babies born preterm: model development and validation using population-level data from England and Wales
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background
Children born preterm face elevated risks of neurodevelopmental impairments across domains. Prior studies have relied on expert-imposed typologies within single domains. This study applies statistical learning to a national database to identify transdomain clusters and their maternal and neonatal predictors.
Methods
Latent class analysis (LCA) was used to derive transdomain clusters from parent-reported visual, auditory, neuromotor, and communication impairments in preterm-born children at two years corrected age using the UK National Neonatal Research Database data (N = 27,261). Replication was conducted in an independent sample from Wales (N = 975). Clusters were clinically validated using cerebral palsy diagnosis, Bayley Scales of Infant and Toddler Development (3rd edition), and global neurodevelopmental delay. Random forest identified cluster-specific and shared predictors.
Findings
Four homogeneous clusters were derived (silhouette score = 0.71) and replicated in Wales with high balanced accuracy (93%): (1) typically developing (84.8%), (2) communication impairments (8.4%), (3) neuro-motor impairments (4.1%), and (4) multiple neuro-morbidity (2.7%). Clusters had high clinical validity and were distinguishable by shared and cluster-specific predictors. Neonatal brain injuries were most predictive of neuro-motor and multiple neuro-morbidity clusters. Birthweight, gestational age, socio-economic deprivation, and sex were stronger predictors of the communication cluster than preterm co-morbidities.
Interpretation
This study provides first evidence of the transdomain nature of neurodevelopmental impairments after preterm birth using LCA. The finding that socio-demographic and perinatal factors rather than co-morbidities increase the risk of communication impairment highlights the importance of environmental modification alongside clinical interventions. Applying data-driven approaches to routinely collected data may offer a cost-effective way to stratify at-risk children and inform targeted support strategies.
Funding
UKRI Medical Research Council.
Children born preterm face elevated risks of neurodevelopmental impairments across domains. Prior studies have relied on expert-imposed typologies within single domains. This study applies statistical learning to a national database to identify transdomain clusters and their maternal and neonatal predictors.
Methods
Latent class analysis (LCA) was used to derive transdomain clusters from parent-reported visual, auditory, neuromotor, and communication impairments in preterm-born children at two years corrected age using the UK National Neonatal Research Database data (N = 27,261). Replication was conducted in an independent sample from Wales (N = 975). Clusters were clinically validated using cerebral palsy diagnosis, Bayley Scales of Infant and Toddler Development (3rd edition), and global neurodevelopmental delay. Random forest identified cluster-specific and shared predictors.
Findings
Four homogeneous clusters were derived (silhouette score = 0.71) and replicated in Wales with high balanced accuracy (93%): (1) typically developing (84.8%), (2) communication impairments (8.4%), (3) neuro-motor impairments (4.1%), and (4) multiple neuro-morbidity (2.7%). Clusters had high clinical validity and were distinguishable by shared and cluster-specific predictors. Neonatal brain injuries were most predictive of neuro-motor and multiple neuro-morbidity clusters. Birthweight, gestational age, socio-economic deprivation, and sex were stronger predictors of the communication cluster than preterm co-morbidities.
Interpretation
This study provides first evidence of the transdomain nature of neurodevelopmental impairments after preterm birth using LCA. The finding that socio-demographic and perinatal factors rather than co-morbidities increase the risk of communication impairment highlights the importance of environmental modification alongside clinical interventions. Applying data-driven approaches to routinely collected data may offer a cost-effective way to stratify at-risk children and inform targeted support strategies.
Funding
UKRI Medical Research Council.
Date Issued
2025-07-01
Date Acceptance
2025-06-02
Citation
EBioMedicine, 2025, 117
ISSN
2352-3964
Publisher
Elsevier
Journal / Book Title
EBioMedicine
Volume
117
Copyright Statement
© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
105811
Date Publish Online
2025-06-17