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Background <p>Very preterm infants are at elevated risk for autism spectrum disorder (ASD), though early identification is challenging due to overlapping neurodevelopmental disorders. While the Bayley Scales of Infant and Toddler Development (BSID) is widely used for follow-up, it remains unclear whether domain-specific developmental trajectories—such as cognition, receptive and expressive communication, and fine and gross motor function assessed by the BSID, Third Edition (BSID-III)—can support the development of a prediction model for ASD risk by preschool age in this population.</p> Methods <p>This population-based multicenter cohort study included infants born &lt; 32 weeks’ gestation in 2011–2018. Neurodevelopment was assessed at 6, 12, and 24 months using domain-specific BSID-III scaled scores. ASD diagnosis was determined at age 5 years using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised. Infants with congenital anomalies or severe sensorimotor impairments were excluded. Developmental trajectories were analyzed using locally estimated scatterplot smoothing. Six machine learning algorithms were used to evaluate ASD prediction based on neonatal risks and longitudinal domain-specific scaled score data.</p> Results <p>Of 583 very-preterm infants, 75 (12.9%) were diagnosed with ASD at preschool age. Infants later diagnosed with ASD exhibited persistently lower cognitive scores across the first two years of life (<i>p</i> &lt; 0.05) and significantly slower development in receptive and expressive communication and fine motor skills during the second year (<i>p</i> &lt; 0.0001 by 24 months) than infants without ASD. Gross motor trajectories did not differ significantly between groups. Integrating neurodevelopmental trajectories up to 24 months with neonatal risk factors improved prediction performance. The Support Vector Machine model yielded 71.8% accuracy (Area Under the Curve 0.69), with sensitivity of 64.2%, specificity of 72.9%, positive predictive value of 24.7%, and negative predictive value of 93.6%.</p> Limitations <p>Although the model shows promise in identifying infants at low likelihood of ASD, its overall predictive performance remains modest. The model was developed in a single regional cohort, potentially limiting generalizability.</p> Conclusions <p>Preterm infants later diagnosed with ASD exhibit distinct, domain-specific developmental trajectories. The model’s high negative predictive value suggests that developmental trajectory phenotypes may support early risk stratification by identifying infants at low likelihood of ASD.</p>

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Early developmental trajectory phenotypes for risk stratification of autism spectrum disorder in very preterm infants: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • Li-Wen Chen,
  • Yi-Tien Li,
  • Chi-Hsiang Chu,
  • Chin-Chin Wu,
  • Ching-Lin Chu,
  • Lan-Wan Wang,
  • Han-Yi Tsai,
  • Chung-Hsin Chiang,
  • Chao-Ching Huang

摘要

Background

Very preterm infants are at elevated risk for autism spectrum disorder (ASD), though early identification is challenging due to overlapping neurodevelopmental disorders. While the Bayley Scales of Infant and Toddler Development (BSID) is widely used for follow-up, it remains unclear whether domain-specific developmental trajectories—such as cognition, receptive and expressive communication, and fine and gross motor function assessed by the BSID, Third Edition (BSID-III)—can support the development of a prediction model for ASD risk by preschool age in this population.

Methods

This population-based multicenter cohort study included infants born < 32 weeks’ gestation in 2011–2018. Neurodevelopment was assessed at 6, 12, and 24 months using domain-specific BSID-III scaled scores. ASD diagnosis was determined at age 5 years using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview–Revised. Infants with congenital anomalies or severe sensorimotor impairments were excluded. Developmental trajectories were analyzed using locally estimated scatterplot smoothing. Six machine learning algorithms were used to evaluate ASD prediction based on neonatal risks and longitudinal domain-specific scaled score data.

Results

Of 583 very-preterm infants, 75 (12.9%) were diagnosed with ASD at preschool age. Infants later diagnosed with ASD exhibited persistently lower cognitive scores across the first two years of life (p < 0.05) and significantly slower development in receptive and expressive communication and fine motor skills during the second year (p < 0.0001 by 24 months) than infants without ASD. Gross motor trajectories did not differ significantly between groups. Integrating neurodevelopmental trajectories up to 24 months with neonatal risk factors improved prediction performance. The Support Vector Machine model yielded 71.8% accuracy (Area Under the Curve 0.69), with sensitivity of 64.2%, specificity of 72.9%, positive predictive value of 24.7%, and negative predictive value of 93.6%.

Limitations

Although the model shows promise in identifying infants at low likelihood of ASD, its overall predictive performance remains modest. The model was developed in a single regional cohort, potentially limiting generalizability.

Conclusions

Preterm infants later diagnosed with ASD exhibit distinct, domain-specific developmental trajectories. The model’s high negative predictive value suggests that developmental trajectory phenotypes may support early risk stratification by identifying infants at low likelihood of ASD.