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Predicting developmental status from 12 to 24 months in infants at risk for Autism Spectrum Disorder: a preliminary
Suzanne L Macari1, Daniel Campbell, Grace W Gengoux
1Child Study Center, Yale University School of Medicine, New Haven, CT, USA. Suzanne.macari@yale.edu
Insights
Predicting Autism Spectrum Disorder (ASD) in toddlers is complex due to varied symptom onset. Early performance on the Autism Diagnostic Observation Schedule may offer clues, but requires detailed analysis for accurate infant screening.
Area of Science:
- Developmental Pediatrics
- Child Psychology
- Neurodevelopmental Disorders
Background:
- Early identification of Autism Spectrum Disorder (ASD) is crucial for timely intervention.
- Predictive markers for ASD in infancy are still being refined.
- The Toddler Module of the Autism Diagnostic Observation Schedule (ADOS) is a key assessment tool.
Purpose of the Study:
- To investigate the association between 12-month ADOS-Toddler Module performance profiles and 24-month developmental status.
- To identify early predictors of ASD in high- and low-risk infants.
- To understand the patterns of symptom emergence in early childhood development.
Main Methods:
- Utilized a nonparametric decision-tree learning algorithm.
- Analyzed individual item performance on the ADOS-Toddler Module at 12 months.
- Correlated 12-month performance with developmental status assessed at 24 months.
Main Results:
- Identified specific sets of 12-month performance profiles as predictors of 24-month developmental status.
- Highlighted that the emergence of ASD symptoms is variable and complex.
- Demonstrated that simple performance metrics may not fully capture the risk for ASD.
Conclusions:
- Predicting ASD in infancy is challenging due to heterogeneous symptom presentation.
- Detailed analysis of specific strengths and deficits is needed for improved early screening.
- Further refinement of diagnostic instruments for ASD in infants requires understanding nuanced developmental trajectories.
Abstract:
The study examined whether performance profiles on individual items of the Toddler Module of the Autism Diagnostic Observation Schedule at 12 months are associated with developmental status at 24 months in infants at high and low risk for developing Autism Spectrum Disorder (ASD). A nonparametric decision-tree learning algorithm identified sets of 12-month predictors of developmental status at 24 months. Results suggest that identification of infants who are likely to exhibit symptoms of ASD at 24 months is complicated by variable patterns of symptom emergence. Fine-grained analyses linking specific profiles of strengths and deficits with specific patterns of symptom emergence will be necessary for further refinement of screening and diagnostic instruments for ASD in infancy.
