title: Predicting developmental status from 12 to 24 months in infants at risk for Autism Spectrum Disorder: a preliminary report.
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.
authors.affiliation: Child Study Center, Yale University School of Medicine, New Haven, CT, USA. Suzanne.macari@yale.edu
authors.forename: Suzanne L
authors.initials: SL
authors.lastname: Macari
authors.forename: Daniel
authors.initials: D
authors.lastname: Campbell
authors.forename: Grace W
authors.initials: GW
authors.lastname: Gengoux
authors.forename: Celine A
authors.initials: CA
authors.lastname: Saulnier
authors.forename: Ami J
authors.initials: AJ
authors.lastname: Klin
authors.forename: Katarzyna
authors.initials: K
authors.lastname: Chawarska
mesh_terms: D000465:Algorithms; D002657:Child Development; D002659:Child Development Disorders, Pervasive / Q000175:diagnosis*; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012307:Risk Factors