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Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age
Shoba S Meera1, Kevin Donovan2, Jason J Wolff3
1Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill; The National Institute of Mental Health and Neurosciences, Bangalore, India.
Insights
A new classifier using the First Year Inventory 2.0 (FYI) can identify infants at 12 months at high risk for Autism Spectrum Disorder (ASD). This tool aids early autism risk detection in infants with a family history of ASD.
Area of Science:
- Developmental Psychology
- Pediatric Neurology
- Machine Learning in Healthcare
Background:
- Early identification of Autism Spectrum Disorder (ASD) is crucial for timely intervention and improved outcomes.
- Familial risk status is a known predictor, but additional screening tools are needed for enhanced risk stratification.
- Parent-report measures offer a scalable approach to gather developmental information in infancy.
Purpose of the Study:
- To develop a classifier using the First Year Inventory 2.0 (FYI) parent-report measure for infants at 12 months of age.
- To identify infants at elevated risk for ASD beyond familial risk status.
- To establish a foundation for refining population-based autism risk estimation.
Main Methods:
- Utilized data from 54 high-familial risk infants later diagnosed with ASD (HR-ASD), 183 high-familial risk infants without ASD (HR-Neg), and 72 low-risk controls.
- Collected First Year Inventory 2.0 (FYI) data at 12 months and diagnostic assessments for ASD at 24 months.
- Employed a data-driven, cross-validated analytical approach to develop and assess the FYI classifier's screening accuracy.
Main Results:
- The developed FYI classifier demonstrated a sensitivity of 0.71 (95% CI: 0.50, 0.91).
- The classifier achieved a specificity of 0.72 (95% CI: 0.49, 0.91).
- These results indicate moderate accuracy in classifying infants at risk for ASD.
Conclusions:
- The FYI classifier shows potential for improving early ASD risk screening in 12-month-old infants with elevated familial risk.
- This approach enhances opportunities for detecting autism risk during infancy.
- Combining parent-report measures with machine learning techniques is a promising strategy for autism screening.
Objective:
This study aimed to develop a classifier for infants at 12 months of age based on a parent-report measure (the First Year Inventory 2.0 [FYI]), for the following reasons: (1) to classify infants at elevated risk, above and beyond that attributable to familial risk status for ASD; and (2) to serve as a starting point to refine an approach for risk estimation in population samples.
Method:
A total of 54 high-familial risk (HR) infants later diagnosed with ASD (HR-ASD), 183 HR infants not diagnosed with ASD at 24 months of age (HR-Neg), and 72 low-risk controls participated in the study. All infants contributed FYI data at 12 months of age and had a diagnostic assessment for ASD at age 24 months. A data-driven, cross-validated analytic approach was used to develop a classifier to determine screening accuracy (eg, sensitivity) of the FYI to classify HR-ASD and HR-Neg.
Results:
The newly developed FYI classifier had an estimated sensitivity of 0.71 (95% CI: 0.50, 0.91) and specificity of 0.72 (95% CI: 0.49, 0.91).
Conclusion:
This classifier demonstrates the potential to improve current screening for ASD risk at 12 months of age in infants already at elevated familial risk for ASD, increasing opportunities for detection of autism risk in infancy. Findings from this study highlight the utility of combining parent-report measures with machine learning approaches.
