Background: Diagnostic shifts at early ages may provide invaluable insights into the nature of separation between autism spectrum disorder (ASD) and typical development. Recent conceptualizations of ASD suggest the condition is only fuzzily separated from non-ASD, with intermediate cases between the two. These intermediate cases may shift along a transition region over time, leading to apparent instability of diagnosis. Methods: We used a cohort of children with high ASD risk, by virtue of having an older sibling with ASD, assessed at 24 months (N = 212) and 36 months (N = 191). We applied machine learning to empirically characterize the classification boundary between ASD and non-ASD, using variables quantifying developmental and adaptive skills. We computed the distance of children to the classification boundary. Results: Children who switched diagnostic labels from 24 to 36 months, in both directions, (dynamic group) had intermediate phenotypic profiles. They were closer to the classification boundary compared to children who had stable diagnoses, both at 24 months (Cohen’s d =.52) and at 36 months (d =.75). The magnitude of change in distance between the two time points was similar for the dynamic and stable groups (Cohen's d =.06), and diagnostic shifts were not associated with a large change. At the individual level, a few children in the dynamic group showed substantial change. Conclusions: Our results suggested that a diagnostic shift was largely due to a slight movement within a transition region between ASD and non-ASD. This fact highlights the need for more vigilant surveillance and intervention strategies. Young children with intermediate phenotypes may have an increased susceptibility to gain or lose their diagnosis at later ages, calling attention to the inherently dynamic nature of early ASD diagnoses.
|Original language||English (US)|
|Number of pages||10|
|Journal||Journal of Child Psychology and Psychiatry and Allied Disciplines|
|Early online date||Apr 7 2021|
|State||Published - Oct 2021|
Bibliographical noteFunding Information:
This research was supported by the grants from the National Institutes of Health (R01‐HD055741, U54‐HD086984, U54‐HD087011, R01‐HD088125, R01‐MH073084, R01‐MH118362, R01‐MH121462, R01‐MH116961, R01‐ES026961) and the Pennsylvania Department of Health (SAP 4100047863, SAP 4100042728). Key points
This research was supported by the grants from the National Institutes of Health (R01-HD055741, U54-HD086984, U54-HD087011, R01-HD088125, R01-MH073084, R01-MH118362, R01-MH121462, R01-MH116961, R01-ES026961) and the Pennsylvania Department of Health (SAP 4100047863, SAP 4100042728). The Infant Brain Imaging Study (IBIS) Network is an NIH-funded Autism Centers of Excellence project and consists of a consortium of 9 universities in the United States and Canada. The study includes children who were previously seen by this network in infancy and who were at high familial risk for autism (based on having an older sibling diagnosed with autism) and a control group of low-risk children (i.e., typically developing children with no family history of autism or other major psychiatric conditions).
© 2021 Association for Child and Adolescent Mental Health.
- Autism spectrum disorders
- machine learning
PubMed: MeSH publication types
- Journal Article