Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age - 21/07/21

Doi : 10.1016/j.jaac.2020.10.015

Shoba S. Meera, PhD ^a,^b,^⁎ , Kevin Donovan, BS ^a, Jason J. Wolff, PhD ^c, Lonnie Zwaigenbaum, MD ^d, Jed T. Elison, PhD ^c, Truong Kinh, PhD ^a, Mark D. Shen, PhD ^a, Annette M. Estes, PhD ^e, Heather C. Hazlett, PhD ^a, Linda R. Watson, EdD ^a, Grace T. Baranek, PhD ^f, Meghan R. Swanson, PhD ^g, Tanya St. John, PhD ^e, Catherine A. Burrows, PhD ^c, Robert T. Schultz, PhD ^h, Stephen R. Dager, MD ^e, Kelly N. Botteron, MD ⁱ, Juhi Pandey, PhD ^h, Joseph Piven, MD ^a
for the
IBIS Network
^a Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill
^b The National Institute of Mental Health and Neurosciences, Bangalore, India
^c University of Minnesota, Minneapolis
^d University of Alberta, Edmonton, Canada; and the Autism Research Centre, Glenrose Rehabilitation Hospital, Edmonton, Canada
^e University of Washington, Seattle
^f University of Southern California, Los Angeles
^g University of Texas at Dallas, Richardson
^h Children’s Hospital of Philadelphia, University of Pennsylvania
ⁱ Washington University in St. Louis, Missouri

^∗Correspondence to Shoba S. Meera, PhD, Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510Carolina Institute for Developmental DisabilitiesUniversity of North Carolina at Chapel HillChapel HillNC27510

Abstract

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.

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Key Words : screening, autism spectrum disorder, high-risk, first year, parent report

Plan

Group Differences on FYI Domain Scores

Predicting ASD Outcome at Age 24 Months Using Individual FYI Items

Abbreviated Version of the FYI

Discussion

	Dr. Meera and Mr. Donovan contributed equally to this research.
	This work was supported by grants through the National Institutes of Health (NIH; R01-HD055741, PI Piven; U54HD079124, PI Piven). Additional funding support has been provided from the Simons Foundation (SFARI; grant 140209, PI Piven). Dr. Meera was supported by the Fulbright-Nehru Post-Doctoral fellowship grant USIEF-2264/FNDPR/2017. The funders had no role in study design, data collection, analysis, data interpretation, or writing of the report.
	Mr. Donovan and Dr. Kinh served as the statistical experts for this research.
	Author Contributions
	Conceptualization: Meera, Piven
	Data curation: Meera, Swanson, Piven
	Formal analysis: Meera, Donovan, Kinh
	Funding acquisition: Piven
	Supervision: Piven
	Writing – original draft: Meera, Donovan, Piven
	Writing – review and editing: Meera, Donovan, Wolff, Zwaigenbaum, Elison, Kinh, Shen, Estes, Hazlett, Watson, Baranek, Swanson, St. John, Burrows, Schultz, Dager, Botteron, Pandey, Piven
	The Infant Brain Imaging Study (IBIS) Network is an NIH-funded Autism Center of Excellence project and consists of a consortium of 8 universities in the US and Canada. Clinical Sites: University of North Carolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; University of Washington: S. Dager, A. Estes, D. Shaw; Washington University: K. Botteron, R. McKinstry, J. Constantino, J. Pruett; Children’s Hospital of Philadelphia: R. Schultz, J. Pandey, S. Paterson; University of Alberta: L. Zwaigenbaum; University of Minnesota: J. Elison, J. Wolff; Data Coordinating Center: Montreal Neurological Institute: A.C. Evans, D.L. Collins, G.B. Pike, V. Fonov, P. Kostopoulos, S. Das, L. MacIntyre; Image Processing Core: University of Utah: G. Gerig; University of North Carolina: M. Styner; Statistical Analysis Core: University of North Carolina: H. Gu.
	Disclosure: Dr. Wolff has received grant or research support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Institute of Mental Health. Dr. Piven has received grant or research support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the US Department of Health and Human Services Administration on Intellectual and Developmental Disabilities, the National Institute of Mental Health, NIH, the National Institute of Neurological Disorders and Stroke, the National Institute of Environmental Health Sciences, and the Simons Foundation. He has served on the John Merck Fund Scientific Advisory Board. He is the Editor-in-Chief of the Journal of Neurodevelopmental Disorders. He is co-inventor of UNC file 16-0185, patent application PCT/US2017/040032, “Methods, Systems, and Computer Readable Media for Utilizing Functional Connectivity Brain Imaging for Diagnosis of a Neurobehavioral Disorder.” Drs. Meera, Zwaigenbaum, Elison, Kinh, Shen, Estes, Hazlett, Watson, Baranek, Swanson, St. John, Burrows, Schultz, Dager, Botteron, Pandey and Mr. Donovan have reported no biomedical financial interests or potential conflicts of interest.