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

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Title: Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.
Authors: Meera, Shoba S.1,2 (AUTHOR) ssmeera@nimhans.ac.in, Donovan, Kevin1 (AUTHOR), Wolff, Jason J.3 (AUTHOR), Zwaigenbaum, Lonnie4 (AUTHOR), Elison, Jed T.3 (AUTHOR), Kinh, Truong1 (AUTHOR), Shen, Mark D.1 (AUTHOR), Estes, Annette M.5 (AUTHOR), Hazlett, Heather C.1 (AUTHOR), Watson, Linda R.1 (AUTHOR), Baranek, Grace T.6 (AUTHOR), Swanson, Meghan R.7 (AUTHOR), St. John, Tanya5 (AUTHOR), Burrows, Catherine A.3 (AUTHOR), Schultz, Robert T.8 (AUTHOR), Dager, Stephen R.5 (AUTHOR), Botteron, Kelly N.9 (AUTHOR), Pandey, Juhi8 (AUTHOR), Piven, Joseph1 (AUTHOR), Meera, Shoba Sreenath10 (AUTHOR)
Source: Journal of the American Academy of Child & Adolescent Psychiatry. Aug2021, Vol. 60 Issue 8, p968-977. 10p.
Subject Terms: *Autism, *Autism spectrum disorders, *Age, *Machine learning, *Research, *Research methodology, *Evaluation research, *Comparative studies, Medical cooperation, Research funding
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Academy of Child & Adolescent Psychiatry is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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DbLabel: Education Research Complete
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Towards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.
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  Data: <searchLink fieldCode="AR" term="%22Meera%2C+Shoba+S%2E%22">Meera, Shoba S.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ssmeera@nimhans.ac.in</i><br /><searchLink fieldCode="AR" term="%22Donovan%2C+Kevin%22">Donovan, Kevin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wolff%2C+Jason+J%2E%22">Wolff, Jason J.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zwaigenbaum%2C+Lonnie%22">Zwaigenbaum, Lonnie</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elison%2C+Jed+T%2E%22">Elison, Jed T.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kinh%2C+Truong%22">Kinh, Truong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Mark+D%2E%22">Shen, Mark D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Estes%2C+Annette+M%2E%22">Estes, Annette M.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hazlett%2C+Heather+C%2E%22">Hazlett, Heather C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Watson%2C+Linda+R%2E%22">Watson, Linda R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Baranek%2C+Grace+T%2E%22">Baranek, Grace T.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Swanson%2C+Meghan+R%2E%22">Swanson, Meghan R.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22St%2E+John%2C+Tanya%22">St. John, Tanya</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burrows%2C+Catherine+A%2E%22">Burrows, Catherine A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schultz%2C+Robert+T%2E%22">Schultz, Robert T.</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dager%2C+Stephen+R%2E%22">Dager, Stephen R.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Botteron%2C+Kelly+N%2E%22">Botteron, Kelly N.</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pandey%2C+Juhi%22">Pandey, Juhi</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Piven%2C+Joseph%22">Piven, Joseph</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meera%2C+Shoba+Sreenath%22">Meera, Shoba Sreenath</searchLink><relatesTo>10</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Academy+of+Child+%26+Adolescent+Psychiatry%22">Journal of the American Academy of Child & Adolescent Psychiatry</searchLink>. Aug2021, Vol. 60 Issue 8, p968-977. 10p.
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  Data: *<searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br />*<searchLink fieldCode="DE" term="%22Autism+spectrum+disorders%22">Autism spectrum disorders</searchLink><br />*<searchLink fieldCode="DE" term="%22Age%22">Age</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br />*<searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaluation+research%22">Evaluation research</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+cooperation%22">Medical cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: <bold>Objective: </bold>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.<bold>Method: </bold>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.<bold>Results: </bold>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).<bold>Conclusion: </bold>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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Academy of Child & Adolescent Psychiatry is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=151467924
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