Identifying Childhood Expressive Language Features That Best Predict Adult Language and Communication Outcome in Individuals With Autism Spectrum Disorder

Purpose Several studies have reported that “useful speech” at 5 years of age predicts outcomes in individuals with autism spectrum disorder (ASD), but this skill has been vaguely defined. This study investigates which specific aspects of expressive language in children with ASD best predict adult language and communication outcomes. Method Language samples from 29 children (ages 47–72 months) enrolled in a longitudinal project (e.g., Lord et al., 2006 ) were transcribed and coded for spoken language features. Hierarchical linear regression was used to compare the following childhood variables as predictors of adult language and communication outcomes: noun diversity, verb diversity, mean length of utterance, and proportion of utterances that were socially motivated. Results Childhood verb diversity was a value-added predictor of all four adult outcome measures (i.e., verbal IQ, Autism Diagnostic Observation Schedule Communication + Social Interaction Algorithm totals, Peabody Picture Vocabulary Test scores, and Vineland Adaptive Behavior Scales Communication Domain scores), while noun diversity and proportion of utterances that were socially motivated were not value-added predictors of any adult outcome measures. In a second set of regression analyses, mean length of utterance was substituted for verb diversity and was a value-added predictor of two out of four adult outcome measures (i.e., verbal IQ and Vineland Adaptive Behavior Scales Communication Domain scores). The pattern of findings for the other predictors remained the same as in the previous analyses. Conclusion These results have implications for our understanding of early language in ASD and for clinical decision making in early childhood.

Predicting Uncertain Multi-Dimensional Adulthood Outcomes From Childhood and Adolescent Data in People Referred to Autism Services

IntroductionAutism spectrum disorder is a highly heterogeneous diagnosis. When a child is referred to autism services or receives a diagnosis of autism spectrum disorder it is not known what their potential adult outcomes could be. We consider the challenge of making predictions of an individual child’s long-term multi-facetted adult outcome, focussing on which aspects are predictable and which are not.MethodsWe used data from 123 adults participating in the Autism Early Diagnosis Cohort. Participants were recruited from age 2 and followed up repeatedly through childhood and adolescence to adulthood. We predicted 14 adult outcome measures including cognitive, behavioral and well-being measures. Continuous outcomes were modeled using lasso regression and ordinal outcomes were modeled using proportional odds regression. Optimism corrected predictive performance was calculated using cross-validation or bootstrap. We also illustrated the prediction of an overall composite formed by weighting outcome measures by priorities elicited from parents.ResultsWe found good predictive performance from age 9 for verbal and non-verbal IQ, and daily living skills. Predictions for symptom severity, hyperactivity and irritability improved with inclusion of behavioral data collected in adolescence but remained modest. For other outcomes covering well-being, depression, and positive and negative affect we found no ability to predict adult outcomes at any age. Predictions of composites based on parental priorities differed in magnitude and precision depending on which parts of the adult outcome were given more weight.ConclusionVerbal and non-verbal IQ, and daily living skills can be predicted well from assessments made in childhood. For other adult outcomes, it is challenging to make meaningful predictions from assessments made in childhood and adolescence using the measures employed in this study. Future work should replicate and validate the present findings in different samples, investigate whether the availability of different measures in childhood and adolescence can improve predictions, and consider systematic differences in priorities.